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More Praise for How to Create a Mind 

“This book is a Rosetta stone for the mystery of human thought. Even more 
remarkably, it is a blueprint for creating artificial consciousness that is as 
persuasive and emotional as our own. Kurzweil deals with the subject of 
consciousness better than anyone from Blackmore to Dennett. His 
persuasive thought experiment is of Einstein quality: It forces recognition 
of the truth.” 

—Martine Rothblatt, chairman and CEO, United Therapeutics; creator 
of Sirius XM Satellite Radio 

“Kurzweil’s book is a shining example of his prodigious ability to 
synthesize ideas from disparate domains and explain them to readers in 
simple, elegant language. Just as Chanute’s Progress in Flying Machines 
ushered in the era of aviation over a century ago, this book is the harbinger 
of the coming revolution in artificial intelligence that will fulfill Kurzweil’s 
own prophecies about it.” 

—Dileep George, AI scientist; pioneer of hierarchical models of the 
neocortex; cofounder of Numenta and Vicarious Systems 

“Ray Kurzweil’s understanding of the brain and artificial intelligence will 
dramatically impact every aspect of our lives, every industry on Earth, and 
how we think about our future. If you care about any of these, read this 

—Peter H. Diamandis, chairman and CEO, X PRIZE; executive 
chairman, Singularity University; author of the New York Times 
bestseller Abundance: The Future Is Better Than You Think 



Transcend: Nine Steps to Living Well Forever 
(with Terry Grossman) 

The Singularity Is Near: When Humans Transcend Biology 

Fantastic Voyage: Live Long Enough to Live Forever 
(with Terry Grossman) 

The Age of Spiritual Machines: 

When Computers Exceed Human Intelligence 

The 10% Solution for a Healthy Life 

The Age of Intelligent Machines 








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80 Strand, London WC2R 0RL, England 

Lirst published in 2012 by Viking Penguin, 

a member of Penguin Group (USA) Inc. 

13579 10 8642 

Copyright © Ray Kurzweil, 2012 

All rights reserved 

“Red” by Amoo Oluseun. Used by permission of the author. 

“The picture’s pretty bleak, gentlemen...” from The Far Side by Gary Larson (November 7, 1985). 

Used by permission of Creators Syndicate. 

Illustration credits 

Page 10: Created by Wolfgang Beyer (Creative Commons Attribution-Share Alike 3.0 License). 21: 
Photo by Timeline (Creative Commons Attribution-Share Alike 3.0 License). 84 (two figures): Prom “The 
Geometric Structure of the Brain Liber Pathways,” by Van J. Wedeen, Douglas L. Rosene, Ruopene Wang, 
Guangping Dai, Larzad Mortazavi, Patric Hagmann, Jon H. Kaas, and Wen-Yih I. Tseng, Science, March 
30, 2012. Reprinted with permission of AAAS (American Association for the Advancement of Science). 

85: Photo provided by Yeatesh (Creative Commons Attribution-Share Alike 3.0 License). 134 (two): 

Images by Marvin Minsky. Used by permission of Marvin Minsky. Some credits appear adjacent to the 
respective images. Other images designed by Ray Kurzweil, illustrated by Laksman Prank. 

Library of Congress Cataloging-in-Publication Data 
Kurzweil, Ray. 

How to create a mind : the secret of human thought revealed / Ray Kurzweil. 

p. cm. 

Includes bibliographical references and index. 

ISBN: 978-1-101-60110-5 

1. Brain—Localization of functions. 2. Self-consciousness (Awareness) 3. Artificial intelligence. I. Title. 

QP385.K87 2012 

612.8’2—dc23 2012027185 

Printed in the United States of America 
Set in Minion Pro with DIN 

Designed by Daniel Lagin 

While the author has made every effort to provide accurate telephone numbers, Internet addresses, and 
other contact information at the time of publication, neither the publisher nor the author assumes any 
responsibility for errors, or for changes that occur after publication. Further, the publisher does not have any 
control over and does not assume any responsibility for author or third-party Web sites or their content. 

No part of this book may be reproduced, scanned, or distributed in any printed or electronic form 
without permission. Please do not participate in or encourage piracy of copyrighted materials in violation of 

the author’s rights. Purchase only authorized editions. 


To Leo Oscar Kurzweil. You are entering an extraordinary world. 


I’d like to express my gratitude to my wife, Sonya, for her loving patience 
through the vicissitudes of the creative process; 

To my children, Ethan and Amy; my daughter-in-law, Rebecca; my sister, 
Enid; and my new grandson, Leo, for their love and inspiration; 

To my mother, Hannah, for supporting my early ideas and inventions, 
which gave me the freedom to experiment at a young age, and for keeping my 
father alive during his long illness; 

To my longtime editor at Viking, Rick Kot, for his leadership, steady and 
insightful guidance, and expert editing; 

To Loretta Barrett, my literary agent for twenty years, for her astute and 
enthusiastic guidance; 

To Aaron Kleiner, my long-term business partner, for his devoted 
collaboration for the past forty years; 

To Amara Angelica for her devoted and exceptional research support; 

To Sarah Black for her outstanding research insights and ideas; 

To Laksman Frank for his excellent illustrations; 

To Sarah Reed for her enthusiastic organizational support; 

To Nanda Barker-Hook for her expert organization of my public events on 
this and other topics; 

To Amy Kurzweil for her guidance on the craft of writing; 

To Cindy Mason for her research support and ideas on AI and the mind- 
body connection; 

To Dileep George for his discerning ideas and insightful discussions by e- 
mail and otherwise; 

To Martine Rothblatt for her dedication to all of the technologies I discuss 
in the book and for our collaborations in developing technologies in these areas; 

To the team, who provided significant research and 
logistical support for this project, including Aaron Kleiner, Amara Angelica, 
Bob Beal, Casey Beal, Celia Black-Brooks, Cindy Mason, Denise Scutellaro, 
Joan Walsh, Giulio Prisco, Ken Linde, Laksman Frank, Maria Ellis, Nanda 

Barker-Hook, Sandi Dube, Sarah Black, Sarah Brangan, and Sarah Reed; 

To the dedicated team at Viking Penguin for all of their thoughtful 
expertise, including Clare Ferraro (president), Carolyn Coleburn (director of 
publicity), Yen Cheong and Langan Kingsley (publicists), Nancy Sheppard 
(director of marketing), Bruce Giffords (production editor), Kyle Davis (editorial 
assistant), Fabiana Van Arsdell (production director), Roland Ottewell (copy 
editor), Daniel Lagin (designer), and Julia Thomas (jacket designer); 

To my colleagues at Singularity University for their ideas, enthusiasm, and 
entrepreneurial energy; 

To my colleagues who have provided inspired ideas reflected in this 
volume, including Barry Ptolemy, Ben Goertzel, David Dalrymple, Dileep 
George, Felicia Ptolemy, Francis Ganong, George Gilder, Larry Janowitch, 
Laura Deming, Lloyd Watts, Martine Rothblatt, Marvin Minsky, Mickey Singer, 
Peter Diamandis, Raj Reddy, Terry Grossman, Tomaso Poggio, and Vlad 

To my peer expert readers, including Ben Goertzel, David Gamez, Dean 
Kamen, Dileep George, Douglas Katz, Harry George, Lloyd Watts, Martine 
Rothblatt, Marvin Minsky, Paul Linsay, Rafael Reif, Raj Reddy, Randal Koene, 
Dr. Stephen Wolfram, and Tomaso Poggio; 

To my in-house and lay readers whose names appear above; 

And, finally, to all of the creative thinkers in the world who inspire me 
every day. 




















The Brain—is wider than the Sky — 
For—put them side by side — 

The one the other will contain 
With ease—and You — beside — 

The Brain is deeper than the sea — 
For—hold them—Blue to Blue — 

The one the other will absorb — 

As Sponges — Buckets — do — 

The Brain is just the weight of God — 
For—Heft them—Pound for Pound — 
And they will differ—if they do — 

As Syllable from Sound 
—Emily Dickinson 

As the most important phenomenon in the universe, intelligence is capable of 
transcending natural limitations, and of transforming the world in its own image. 
In human hands, our intelligence has enabled us to overcome the restrictions of 
our biological heritage and to change ourselves in the process. We are the only 
species that does this. 

The story of human intelligence starts with a universe that is capable of 
encoding information. This was the enabling factor that allowed evolution to 
take place. How the universe got to be this way is itself an interesting story. The 
standard model of physics has dozens of constants that need to be precisely what 
they are, or atoms would not have been possible, and there would have been no 
stars, no planets, no brains, and no books on brains. That the laws of physics are 
so precisely tuned to have allowed the evolution of information appears to be 
incredibly unlikely. Yet by the anthropic principle, we would not be talking 

about it if it were not the case. Where some people see a divine hand, others see 
a multiverse spawning an evolution of universes with the boring (non- 
information-bearing) ones dying out. But regardless of how our universe got to 
be the way it is, we can start our story with a world based on information. 

The story of evolution unfolds with increasing levels of abstraction. Atoms 
—especially carbon atoms, which can create rich information structures by 
linking in four different directions—formed increasingly complex molecules. As 
a result, physics gave rise to chemistry. 

A billion years later, a complex molecule called DNA evolved, which could 
precisely encode lengthy strings of information and generate organisms 
described by these “programs.” As a result, chemistry gave rise to biology. 

At an increasingly rapid rate, organisms evolved communication and 
decision networks called nervous systems, which could coordinate the 
increasingly complex parts of their bodies as well as the behaviors that 
facilitated their survival. The neurons making up nervous systems aggregated 
into brains capable of increasingly intelligent behaviors. In this way, biology 
gave rise to neurology, as brains were now the cutting edge of storing and 
manipulating information. Thus we went from atoms to molecules to DNA to 
brains. The next step was uniquely human. 

The mammalian brain has a distinct aptitude not found in any other class of 
animal. We are capable of hierarchical thinking, of understanding a structure 
composed of diverse elements arranged in a pattern, representing that 
arrangement with a symbol, and then using that symbol as an element in a yet 
more elaborate configuration. This capability takes place in a brain structure 
called the neocortex, which in humans has achieved a threshold of sophistication 
and capacity such that we are able to call these patterns ideas. Through an 
unending recursive process we are capable of building ideas that are ever more 
complex. We call this vast array of recursively linked ideas knowledge. Only 
Homo sapiens have a knowledge base that itself evolves, grows exponentially, 
and is passed down from one generation to another. 

Our brains gave rise to yet another level of abstraction, in that we have used 
the intelligence of our brains plus one other enabling factor, an opposable 
appendage—the thumb—to manipulate the environment to build tools. These 
tools represented a new form of evolution, as neurology gave rise to technology. 
It is only because of our tools that our knowledge base has been able to grow 
without limit. 

Our first invention was the story: spoken language that enabled us to 
represent ideas with distinct utterances. With the subsequent invention of written 
language we developed distinct shapes to symbolize our ideas. Libraries of 

written language vastly extended the ability of our unaided brains to retain and 
extend our knowledge base of recursively structured ideas. 

There is some debate as to whether other species, such as chimpanzees, 
have the ability to express hierarchical ideas in language. Chimps are capable of 
learning a limited set of sign language symbols, which they can use to 
communicate with human trainers. It is clear, however, that there are distinct 
limits to the complexity of the knowledge structures with which chimps are 
capable of dealing. The sentences that they can express are limited to specific 
simple noun-verb sequences and are not capable of the indefinite expansion of 
complexity characteristic of humans. For an entertaining example of the 
complexity of human-generated language, just read one of the spectacular 
multipage-length sentences in a Gabriel Garda Marquez story or novel—his six- 
page story “The Last Voyage of the Ghost” is a single sentence and works quite 
well in both Spanish and the English translation.- 

The primary idea in my three previous books on technology ( The Age of 
Intelligent Machines, written in the 1980s and published in 1989; The Age of 
Spiritual Machines, written in the mid- to late 1990s and published in 1999; and 
The Singularity Is Near, written in the early 2000s and published in 2005) is that 
an evolutionary process inherently accelerates (as a result of its increasing levels 
of abstraction) and that its products grow exponentially in complexity and 
capability. I call this phenomenon the law of accelerating returns (LOAR), and it 
pertains to both biological and technological evolution. The most dramatic 
example of the LOAR is the remarkably predictable exponential growth in the 
capacity and price/performance of information technologies. The evolutionary 
process of technology led invariably to the computer, which has in turn enabled a 
vast expansion of our knowledge base, permitting extensive links from one area 
of knowledge to another. The Web is itself a powerful and apt example of the 
ability of a hierarchical system to encompass a vast array of knowledge while 
preserving its inherent structure. The world itself is inherently hierarchical— 
trees contain branches; branches contain leaves; leaves contain veins. Buildings 
contain floors; floors contain rooms; rooms contain doorways, windows, walls, 
and floors. 

We have also developed tools that are now enabling us to understand our 
own biology in precise information terms. We are rapidly reverse-engineering 
the information processes that underlie biology, including that of our brains. We 
now possess the object code of life in the form of the human genome, an 
achievement that was itself an outstanding example of exponential growth, in 
that the amount of genetic data the world has sequenced has approximately 

doubled every year for the past twenty years.- We now have the ability to 
simulate on computers how sequences of base pairs give rise to sequences of 
amino acids that fold up into three-dimensional proteins, from which all of 
biology is constructed. The complexity of proteins for which we can simulate 
protein folding has been steadily increasing as computational resources continue 
to grow exponentially.- We can also simulate how proteins interact with one 
another in an intricate three-dimensional dance of atomic forces. Our growing 
understanding of biology is one important facet of discovering the intelligent 
secrets that evolution has bestowed on us and then using these biologically 
inspired paradigms to create ever more intelligent technology. 

There is now a grand project under way involving many thousands of 
scientists and engineers working to understand the best example we have of an 
intelligent process: the human brain. It is arguably the most important effort in 
the history of the human-machine civilization. In The Singularity Is Near I made 
the case that one corollary of the law of accelerating returns is that other 
intelligent species are likely not to exist. To summarize the argument, if they 
existed we would have noticed them, given the relatively brief time that elapses 
between a civilization’s possessing crude technology (consider that in 1850 the 
fastest way to send nationwide information was the Pony Express) to its 
possessing technology that can transcend its own planet.- From this perspective, 
reverse-engineering the human brain may be regarded as the most important 
project in the universe. 

The goal of the project is to understand precisely how the human brain 
works, and then to use these revealed methods to better understand ourselves, to 
fix the brain when needed, and—most relevant to the subject of this book—to 
create even more intelligent machines. Keep in mind that greatly amplifying a 
natural phenomenon is precisely what engineering is capable of doing. As an 
example, consider the rather subtle phenomenon of Bernoulli’s principle, which 
states that there is slightly less air pressure over a moving curved surface than 
over a moving flat one. The mathematics of how Bernoulli’s principle produces 
wing lift is still not yet fully settled among scientists, yet engineering has taken 
this delicate insight, focused its powers, and created the entire world of aviation. 

In this book I present a thesis I call the pattern recognition theory of mind 
(PRTM), which, I argue, describes the basic algorithm of the neocortex (the 
region of the brain responsible for perception, memory, and critical thinking). In 
the chapters ahead I describe how recent neuroscience research, as well as our 
own thought experiments, leads to the inescapable conclusion that this method is 
used consistently across the neocortex. The implication of the PRTM combined 

with the LOAR is that we will be able to engineer these principles to vastly 
extend the powers of our own intelligence. 

Indeed this process is already well under way. There are hundreds of tasks 
and activities formerly the sole province of human intelligence that can now be 
conducted by computers, usually with greater precision and at a vastly greater 
scale. Every time you send an e-mail or connect a cell phone call, intelligent 
algorithms optimally route the information. Obtain an electrocardiogram, and it 
comes back with a computer diagnosis that rivals that of doctors. The same is 
true for blood cell images. Intelligent algorithms automatically detect credit card 
fraud, fly and land airplanes, guide intelligent weapons systems, help design 
products with intelligent computer-aided design, keep track of just-in-time 
inventory levels, assemble products in robotic factories, and play games such as 
chess and even the subtle game of Go at master levels. 

Millions of people witnessed the IBM computer named Watson play the 
natural-language game of Jeopardy! and obtain a higher score than the best two 
human players in the world combined. It should be noted that not only did 
Watson read and “understand” the subtle language in the Jeopardy! query (which 
includes such phenomena as puns and metaphors), but it obtained the knowledge 
it needed to come up with a response from understanding hundreds of millions 
of pages of natural-language documents including Wikipedia and other 
encyclopedias on its own. It needed to master virtually every area of human 
intellectual endeavor, including history, science, literature, the arts, culture, and 
more. IBM is now working with Nuance Speech Technologies (formerly 
Kurzweil Computer Products, my first company) on a new version of Watson 
that will read medical literature (essentially all medical journals and leading 
medical blogs) to become a master diagnostician and medical consultant, using 
Nuance’s clinical language-understanding technologies. Some observers have 
argued that Watson does not really “understand” the Jeopardy! queries or the 
encyclopedias it has read because it is just engaging in “statistical analysis.” A 
key point I will describe here is that the mathematical techniques that have 
evolved in the field of artificial intelligence (such as those used in Watson and 
Siri, the iPhone assistant) are mathematically very similar to the methods that 
biology evolved in the form of the neocortex. If understanding language and 
other phenomena through statistical analysis does not count as true 
understanding, then humans have no understanding either. 

Watson’s ability to intelligently master the knowledge in natural-language 
documents is coming to a search engine near you, and soon. People are already 
talking to their phones in natural language (via Siri, for example, which was also 
contributed to by Nuance). These natural-language assistants will rapidly 

become more intelligent as they utilize more of the Watson-like methods and as 
Watson itself continues to improve. 

The Google self-driving cars have logged 200,000 miles in the busy cities 
and towns of California (a figure that will undoubtedly be much higher by the 
time this book hits the real and virtual shelves). There are many other examples 
of artificial intelligence in today’s world, and a great deal more on the horizon. 

As further examples of the LOAR, the spatial resolution of brain scanning 
and the amount of data we are gathering on the brain are doubling every year. 
We are also demonstrating that we can turn this data into working models and 
simulations of brain regions. We have succeeded in reverse-engineering key 
functions of the auditory cortex, where we process information about sound; the 
visual cortex, where we process information from our sight; and the cerebellum, 
where we do a portion of our skill formation (such as catching a fly ball). 

The cutting edge of the project to understand, model, and simulate the 
human brain is to reverse-engineer the cerebral neocortex, where we do our 
recursive hierarchical thinking. The cerebral cortex, which accounts for 80 
percent of the human brain, is composed of a highly repetitive structure, 
allowing humans to create arbitrarily complex structures of ideas. 

In the pattern recognition theory of mind, I describe a model of how the 
human brain achieves this critical capability using a very clever structure 
designed by biological evolution. There are details in this cortical mechanism 
that we do not yet fully understand, but we know enough about the functions it 
needs to perform that we can nonetheless design algorithms that accomplish the 
same purpose. By beginning to understand the neocortex, we are now in a 
position to greatly amplify its powers, just as the world of aviation has vastly 
amplified the powers of Bernoulli’s principle. The operating principle of the 
neocortex is arguably the most important idea in the world, as it is capable of 
representing all knowledge and skills as well as creating new knowledge. It is 
the neocortex, after all, that has been responsible for every novel, every song, 
every painting, every scientific discovery, and the multifarious other products of 
human thought. 

There is a great need in the field of neuroscience for a theory that ties 
together the extremely disparate and extensive observations that are being 
reported on a daily basis. A unified theory is a crucial requirement in every 
major area of science. In chapter 1 I’ll describe how two daydreamers unified 
biology and physics, fields that had previously seemed hopelessly disordered 
and varied, and then address how such a theory can be applied to the landscape 
of the brain. 

Today we often encounter great celebrations of the complexity of the 

human brain. Google returns some 30 million links for a search request for 
quotations on that topic. (It is impossible to translate this into the number of 
actual quotations it is returning, however, as some of the Web sites linked have 
multiple quotes, and some have none.) James D. Watson himself wrote in 1992 
that “the brain is the last and grandest biological frontier, the most complex thing 
we have yet discovered in our universe.” He goes on to explain why he believes 
that “it contains hundreds of billions of cells interlinked through trillions of 
connections. The brain boggles the mind.”- 

I agree with Watson’s sentiment about the brain’s being the grandest 
biological frontier, but the fact that it contains many billions of cells and trillions 
of connections does not necessarily make its primary method complex if we can 
identify readily understandable (and recreatable) patterns in those cells and 
connections, especially massively redundant ones. 

Let’s think about what it means to be complex. We might ask, is a forest 
complex? The answer depends on the perspective you choose to take. You could 
note that there are many thousands of trees in the forest and that each one is 
different. You could then go on to note that each tree has thousands of branches 
and that each branch is completely different. Then you could proceed to describe 
the convoluted vagaries of a single branch. Your conclusion might be that the 
forest has a complexity beyond our wildest imagination. 

But such an approach would literally be a failure to see the forest for the 
trees. Certainly there is a great deal of fractal variation among trees and 
branches, but to correctly understand the principles of a forest you would do 
better to start by identifying the distinct patterns of redundancy with stochastic 
(that is, random) variation that are found there. It would be fair to say that the 
concept of a forest is simpler than the concept of a tree. 

Thus it is with the brain, which has a similar enormous redundancy, 
especially in the neocortex. As I will describe in this book, it would be fair to say 
that there is more complexity in a single neuron than in the overall structure of 
the neocortex. 

My goal in this book is definitely not to add another quotation to the 
millions that already exist attesting to how complex the brain is, but rather to 
impress you with the power of its simplicity. I will do so by describing how a 
basic ingenious mechanism for recognizing, remembering, and predicting a 
pattern, repeated in the neocortex hundreds of millions of times, accounts for the 
great diversity of our thinking. Just as an astonishing diversity of organisms 
arises from the different combinations of the values of the genetic code found in 
nuclear and mitochondrial DNA, so too does an astounding array of ideas, 
thoughts, and skills form based on the values of the patterns (of connections and 

synaptic strengths) found in and between our neocortical pattern recognizers. As 
MIT neuroscientist Sebastian Seung says, “Identity lies not in our genes, but in 
the connections between our brain cells. 

We need to distinguish between true complexity of design and apparent 
complexity. Consider the famous Mandelbrot set, the image of which has long 
been a symbol of complexity. To appreciate its apparent complication, it is useful 
to zoom in on its image (which you can access via the links in this endnote).- 
There is endless intricacy within intricacy, and they are always different. Yet the 
design—the formula—for the Mandelbrot set couldn’t be simpler. It is six 
characters long: Z = Z 2 + C, in which Z is a “complex” number (meaning a pair 
of numbers) and C is a constant. It is not necessary to fully understand the 
Mandelbrot function to see that it is simple. This formula is applied iteratively 
and at every level of a hierarchy. The same is true of the brain. Its repeating 
structure is not as simple as that of the six-character formula of the Mandelbrot 
set, but it is not nearly as complex as the millions of quotations on the brain’s 
complexity would suggest. This neocortical design is repeated over and over at 
every level of the conceptual hierarchy represented by the neocortex. Einstein 
articulated my goals in this book well when he said that “any intelligent fool can 
make things bigger and more complex...but it takes...a lot of courage to move 
in the opposite direction.” 

One view of the display of the Mandelbrot set, a simple formula that is 
iteratively applied. As one zooms in on the display, the images constantly 
change in apparently complex ways. 

So far I have been talking about the brain. But what about the mind? For 
example, how does a problem-solving neocortex attain consciousness? And 
while we’re on the subject, just how many conscious minds do we have in our 
brain? There is evidence that suggests there may be more than one. 

Another pertinent question about the mind is, what is free will, and do we 
have it? There are experiments that appear to show that we start implementing 
our decisions before we are even aware that we have made them. Does that 
imply that free will is an illusion? 

Finally, what attributes of our brain are responsible for forming our 
identity? Am I the same person I was six months ago? Clearly I am not exactly 
the same as I was then, but do I have the same identity? 

We’ll review what the pattern recognition theory of mind implies about 
these age-old questions. 



Darwin’s theory of natural selection came very late in the history of 

Was it delayed because it opposed revealed truth, because it was an 
entirely new subject in the history of science, because it was characteristic 
only of living things, or because it dealt with purpose and final causes 
without postulating an act of creation? I think not. Darwin simply 
discovered the role of selection, a kind of causality very different from the 
push-pull mechanisms of science up to that time. The origin of a fantastic 
variety of living things could be explained by the contribution of which 
novel features, possibly of random provenance, made it to survival. There 
was little or nothing in physical or biological science that foreshadowed 
selection as a causal principle. 

—B. F. Skinner 

Nothing is at last sacred but the integrity of your own mind. 

—Ralph Waldo Emerson 

A Metaphor from Geology 

In the early nineteenth century geologists pondered a fundamental question. 
Great caverns and canyons such as the Grand Canyon in the United States and 
Vikos Gorge in Greece (reportedly the deepest canyon in the world) existed all 
across the globe. How did these majestic formations get there? 

Invariably there was a stream of water that appeared to take advantage of 
the opportunity to course through these natural structures, but prior to the mid¬ 
nineteenth century, it had seemed absurd that these gentle flows could be the 
creator of such huge valleys and cliffs. British geologist Charles Lyell (1797- 
1875), however, proposed that it was indeed the movement of water that had 
carved out these major geological modifications over great periods of time, 
essentially one grain of rock at a time. This proposal was initially met with 
ridicule, but within two decades Lyell’s thesis achieved mainstream acceptance. 

One person who was carefully watching the response of the scientific 
community to Lyell’s radical thesis was English naturalist Charles Darwin 
(1809-1882). Consider the situation in biology around 1850. The field was 
endlessly complex, faced with countless species of animals and plants, any one 
of which presented great intricacy. If anything, most scientists resisted any 
attempt to provide a unifying theory of nature’s dazzling variation. This diversity 
served as a testament to the glory of God’s creation, not to mention to the 
intelligence of the scientists who were capable of mastering it. 

Darwin approached the problem of devising a general theory of species by 
making an analogy with Lyell’s thesis to account for the gradual changes in the 
features of species over many generations. He combined this insight with his 
own thought experiments and observations in his famous Voyage of the Beagle. 
Darwin argued that in each generation the individuals that could best survive in 
their ecological niche would be the individuals to create the next generation. 

On November 22, 1859, Darwin’s book On the Origin of Species went on 
sale, and in it he made clear his debt to Lyell: 

I am well aware that this doctrine of natural selection, exemplified in 
the above imaginary instances, is open to the same objections which were at 
first urged against Sir Charles Lyell’s noble views on “the modern changes 
of the earth, as illustrative of geology”; but we now very seldom hear the 

action, for instance, of the coast-waves called a trifling and insignificant 
cause, when applied to the excavation of gigantic valleys or to the 
formation of the longest lines of inland cliffs. Natural selection can act only 
by the preservation and accumulation of infinitesimally small inherited 
modifications, each profitable to the preserved being; and as modern 
geology has almost banished such views as the excavation of a great valley 
by a single diluvial wave, so will natural selection, if it be a true principle, 
banish the belief of the continued creation of new organic beings, or of any 
great and sudden modification in their structure.- 

Charles Darwin, author of On the Origin of Species, which 
established the idea of biological evolution. 

There are always multiple reasons why big new ideas are resisted, and it is 
not hard to identify them in Darwin’s case. That we were descended not from 
God but from monkeys, and before that, worms, did not sit well with many 
commentators. The implication that our pet dog was our cousin, as was the 
caterpillar, not to mention the plant it walked on (a millionth or billionth cousin, 
perhaps, but still related), seemed a blasphemy to many. 

But the idea quickly caught on because it brought coherence to what had 
previously been a plethora of apparently unrelated observations. By 1872, with 
the publication of the sixth edition of On the Origin of Species, Darwin added 
this passage: “As a record of a former state of things, I have retained in the 
foregoing paragraphs...several sentences which imply that naturalists believe in 
the separate creation of each species; and I have been much censured for having 

thus expressed myself. But undoubtedly this was the general belief when the first 
edition of the present work appeared.... Now things are wholly changed, and 
almost every naturalist admits the great principle of evolution.”- 

Over the next century Darwin’s unifying idea deepened. In 1869, only a 
decade after the original publication of On the Origin of Species, Swiss 
physician Friedrich Miescher (1844-1895) discovered a substance he called 
“nuclein” in the cell nucleus, which turned out to be DNA." In 1927 Russian 
biologist Nikolai Koltsov (1872-1940) described what he called a “giant 
hereditary molecule,” which he said was composed of “two mirror strands that 
would replicate in a semi-conservative fashion using each strand as a template.” 
His finding was also condemned by many. The communists considered it to be 
fascist propaganda, and his sudden, unexpected death has been attributed to the 
secret police of the Soviet Union.- In 1953, nearly a century after the publication 
of Darwin’s seminal book, American biologist James D. Watson (born in 1928) 
and English biologist Francis Crick (1916-2004) provided the first accurate 
characterization of the structure of DNA, describing it as a double helix of two 
long twisting molecules.- It is worth pointing out that their finding was based on 
what is now known as “photo 51,” taken by their colleague Rosalind Franklin 
using X-ray crystallography, which was the first representation that showed the 
double helix. Given the insights derived from Franklin’s image, there have been 
suggestions that she should have shared in Watson and Crick’s Nobel Prize.- 

Rosalind Franklin took the critical picture of DNA (using X-ray 
crystallography) that enabled Watson and Crick to accurately describe the 
structure of DNA for the first time. 

With the description of a molecule that could code the program of biology, 
a unifying theory of biology was now firmly in place. It provided a simple and 
elegant foundation to all of life. Depending only on the values of the base pairs 
that make up the DNA strands in the nucleus (and to a lesser degree the 
mitochondria), an organism would mature into a blade of grass or a human 
being. This insight did not eliminate the delightful diversity of nature, but we 
now understand that the extraordinary diversity of nature stems from the great 
assortment of structures that can be coded on this universal molecule. 

Riding on a Light Beam 

At the beginning of the twentieth century the world of physics was upended 
through another series of thought experiments. In 1879 a boy was born to a 
German engineer and a housewife. He didn’t start to talk until the age of three 
and was reported to have had problems in school at the age of nine. At sixteen he 
was daydreaming about riding on a moonbeam. 

This young boy was aware of English mathematician Thomas Young’s 
(1773-1829) experiment in 1803 that established that light is composed of 
waves. The conclusion at that time was that light waves must be traveling 
through some sort of medium; after all, ocean waves traveled through water and 
sound waves traveled through air and other materials. Scientists called the 
medium through which light waves travel the “ether.” The boy was also aware of 
the 1887 experiment by American scientists Albert Michelson (1852-1931) and 
Edward Morley (1838-1923) that attempted to confirm the existence of the 
ether. That experiment was based on the analogy of traveling in a rowboat up- 
and downstream in a river. If you are paddling at a fixed speed, then your speed 
as measured from the shore will be faster if you are paddling with the stream as 
opposed to going against it. Michelson and Morley assumed that light would 
travel through the ether at a constant speed (that is, at the speed of light). They 
reasoned that the speed of sunlight when Earth is traveling toward the sun in its 
orbit (as measured from our vantage point on Earth) versus its apparent speed 
when Earth is traveling away from the sun must be different (by twice the speed 
of Earth). Proving that would confirm the existence of the ether. However, what 
they discovered was that there was no difference in the speed of the sunlight 
passing Earth regardless of where Earth was in its orbit. Their findings disproved 
the idea of the “ether,” but what was really going on? This remained a mystery 
for almost two decades. 

As this German teenager imagined riding alongside a light wave, he 
reasoned that he should be seeing the light waves frozen, in the same way that a 
train would appear not to be moving if you rode alongside it at the same speed as 
the train. Yet he realized that this was impossible, because the speed of light is 
supposed to be constant regardless of your own movement. So he imagined 
instead riding alongside the light beam but at a somewhat slower speed. What if 
he traveled at 90 percent of the speed of light? If light beams are like trains, he 
reasoned, then he should see the light beam traveling ahead of him at 10 percent 

of the speed of light. Indeed, that would have to be what observers on Earth 
would see. But we know that the speed of light is a constant, as the Michelson- 
Morley experiment had shown. Thus he would necessarily see the light beam 
traveling ahead of him at the full speed of light. This seemed like a contradiction 
—how could it be possible? 

The answer became evident to the German boy, whose name, incidentally, 
was Albert Einstein (1879-1955), by the time he turned twenty-six. Obviously— 
to young Master Einstein —time itself must have slowed down for him. He 
explains his reasoning in a paper published in 19057 If observers on Earth were 
to look at the young man’s watch they would see it ticking ten times slower. 
Indeed, when he got back to Earth, his watch would show that only 10 percent as 
much time had passed (ignoring, for the moment, acceleration and deceleration). 
From his perspective, however, his watch was ticking normally and the light 
beam next to him was traveling at the speed of light. The ten-times slowdown in 
the speed of time itself (relative to clocks on Earth) fully explains the apparent 
discrepancies in perspective. In the extreme, the slowdown in the passage of 
time would reach zero once the speed of travel reached the speed of light; hence 
it was impossible to ride along with the light beam. Although it was impossible 
to travel at the speed of light, it turned out not to be theoretically impossible to 
move faster than the light beam. Time would then move backward. 

This resolution seemed absurd to many early critics. How could time itself 
slow down, based only on someone’s speed of movement? Indeed, for eighteen 
years (from the time of the Michelson-Morley experiment), other thinkers had 
been unable to see a conclusion that was so obvious to Master Einstein. The 
many others who had considered this problem through the latter part of the 
nineteenth century had essentially “fallen off the horse” in terms of following 
through on the implications of a principle, sticking instead to their preconceived 
notions of how reality must work. (I should probably change that metaphor to 
“fallen off the light beam.”) 

Einstein’s second mind experiment was to consider himself and his brother 
flying through space. They are 186,000 miles apart. Einstein wants to move 
faster but he also desires to keep the distance between them the same. So he 
signals his brother with a flashlight each time he wants to accelerate. Since he 
knows that it will take one second for the signal to reach his brother, he waits a 
second (after sending the signal) to initiate his own acceleration. Each time the 
brother receives the signal he immediately accelerates. In this way the two 
brothers accelerate at exactly the same time and therefore remain a constant 
distance apart. 

But now consider what we would see if we were standing on Earth. If the 

brothers were moving away from us (with Albert in the lead), it would appear to 
take less than a second for the light to reach the brother, because he is traveling 
toward the light. Also we would see Albert’s brother’s clock as slowing down 
(as his speed increases as he is closer to us). For both of these reasons we would 
see the two brothers getting closer and closer and eventually colliding. Yet from 
the perspective of the two brothers, they remain a constant 186,000 miles apart. 

How can this be? The answer— obviously —is that distances contract 
parallel to the motion (but not perpendicular to it). So the two Einstein brothers 
are getting shorter (assuming they are flying headfirst) as they get faster. This 
bizarre conclusion probably lost Einstein more early fans than the difference in 
the passage of time. 

During the same year, Einstein considered the relationship of matter and 
energy with yet another mind experiment. Scottish physicist James Clerk 
Maxwell had shown in the 1850s that particles of light called photons had no 
mass but nonetheless carried momentum. As a child I had a device called a 
Crookes radiometer,- which consisted of an airtight glass bulb that contained a 
partial vacuum and a set of four vanes that rotated on a spindle. The vanes were 
white on one side and black on the other. The white side of each vane reflected 
light, and the black side absorbed light. (That’s why it is cooler to wear a white 
T-shirt on a hot day than a black one.) When a light was shined on the device, 
the vanes rotated, with the dark sides moving away from the light. This is a 
direct demonstration that photons carry enough momentum to actually cause the 
vanes of the radiometer to move.- 

The issue that Einstein struggled with is that momentum is a function of 
mass: Momentum is equal to mass times velocity. Thus a locomotive traveling at 
30 miles per hour has a lot more momentum than, say, an insect traveling at the 
same speed. How, then, could there be positive momentum for a particle with 
zero mass? 

Einstein’s mind experiment consisted of a box floating in space. A photon is 
emitted inside the box from the left toward the right side. The total momentum 
of the system needs to be conserved, so the box would have to recoil to the left 
when the photon was emitted. After a certain amount of time, the photon collides 
with the right side of the box, transferring its momentum back to the box. The 
total momentum of the system is again conserved, so the box now stops moving. 

A Crookes radiometer—the vane with four wings rotates when light 
shines on it. 

So far so good. But consider the perspective from the vantage point of Mr. 
Einstein, who is watching the box from the outside. He does not see any outside 
influence on the box: No particles—with or without mass—hit it, and nothing 
leaves it. Yet Mr. Einstein, according to the scenario above, sees the box move 
temporarily to the left and then stop. According to our analysis, each photon 
should permanently move the box to the left. Since there have been no external 
effects on the box or from the box, its center of mass must remain in the same 
place. Yet the photon inside the box, which moves from left to right, cannot 
change the center of mass, because it has no mass. 

Or does it? Einstein’s conclusion was that since the photon clearly has 
energy, and has momentum, it must also have a mass equivalent. The energy of 
the moving photon is entirely equivalent to a moving mass. We can compute 
what that equivalence is by recognizing that the center of mass of the system 
must remain stationary during the movement of the photon. Working out the 
math, Einstein showed that mass and energy are equivalent and are related by a 
simple constant. However, there was a catch: The constant might be simple, but 
it turned out to be enormous; it was the speed of light squared (about 1.7 x 10 17 
meters 2 per second 2 —that is, 17 followed by 16 zeroes). Hence we get Einstein’s 
famous E = me 2 .— Thus one ounce (28 grams) of mass is equivalent to 600,000 
tons of TNT. Einstein’s letter of August 2, 1939, to President Roosevelt 
informing him of the potential for an atomic bomb based on this formula ushered 

in the atomic age.— 

You might think that this should have been obvious earlier, given that 
experimenters had noticed that the mass of radioactive substances decreased as a 
result of radiation over time. It was assumed, however, that radioactive 
substances contained a special high-energy fuel of some sort that was burning 
off. That assumption is not all wrong; it’s just that the fuel that was being 
“burned off” was simply mass. 

There are several reasons why I have opened this book with Darwin’s and 
Einstein’s mind experiments. First of all, they show the extraordinary power of 
the human brain. Without any equipment at all other than a pen and paper to 
draw the stick figures in these simple mind experiments and to write down the 
fairly simple equations that result from them, Einstein was able to overthrow the 
understanding of the physical world that dated back two centuries, deeply 
influence the course of history (including World War II), and usher in the nuclear 

It is true that Einstein relied on a few experimental findings of the 
nineteenth century, although these experiments also did not use sophisticated 
equipment. It is also true that subsequent experimental validation of Einstein’s 
theories has used advanced technologies, and if these had not been developed we 
would not have the validation that we possess today that Einstein’s ideas are 
authentic and significant. However, such factors do not detract from the fact that 
these famous thought experiments reveal the power of human thinking at its 

Einstein is widely regarded as the leading scientist of the twentieth century 
(and Darwin would be a good contender for that honor in the nineteenth 
century), yet the mathematics underlying his theories is ultimately not very 
complicated. The thought experiments themselves were straightforward. We 
might wonder, then, in what respect could Einstein be considered particularly 
smart. We’ll discuss later exactly what it was that he was doing with his brain 
when he came up with his theories, and where that quality resides. 

Conversely, this history also demonstrates the limitations of human 
thinking. Einstein was able to ride his light beam without falling off (albeit he 
concluded that it was impossible to actually ride a light beam), but how many 
thousands of other observers and thinkers were completely unable to think 
through these remarkably uncomplicated exercises? One common failure is the 
difficulty that most people have in discarding and transcending the ideas and 
perspectives of their peers. There are other inadequacies as well, which we will 
discuss in more detail after we have examined how the neocortex works. 

A Unified Model of the Neocortex 

The most important reason I am sharing what are perhaps the most famous 
thought experiments in history is as an introduction to using the same approach 
with respect to the brain. As you will see, we can get remarkably far in figuring 
out how human intelligence works through some simple mind experiments of 
our own. Considering the subject matter involved, mind experiments should be a 
very appropriate approach. 

If a young man’s idle thoughts and the use of no equipment other than pen 
and paper were sufficient to revolutionize our understanding of physics, then we 
should be able to make reasonable progress with a phenomenon with which we 
are much more familiar. After all, we experience our thinking every moment of 
our waking lives—and our dreaming lives as well. 

After we construct a model of how thinking works through this process of 
self-reflection, we’ll examine to what extent we can confirm it through the latest 
observations of actual brains and the state of the art in re-creating these 
processes in machines. 



I very rarely think in words at all. A thought comes, and I may try to 
express it in words afterwards. 

—Albert Einstein 

The brain is a three-pound mass you can hold in your hand that can 
conceive of a universe a hundred billion light years across. 

—Marian Diamond 

What seems astonishing is that a mere three-pound object, made of the 
same atoms that constitute everything else under the sun, is capable of 
directing virtually everything that humans have done: flying to the moon 
and hitting seventy home runs, writing Hamlet and building the Taj Mahal 
—even unlocking the secrets of the brain itself. 

—Joel Havemann 

I started thinking about thinking around 1960, the same year that I discovered 
the computer. You would be hard pressed today to find a twelve-year-old who 
does not use a computer, but back then there were only a handful of them in my 
hometown of New York City. Of course these early devices did not fit in your 
hand, and the first one I got access to took up a large room. In the early 1960s I 
did some programming on an IBM 1620 to do analyses of variance (a statistical 
test) on data that had been collected by studying a program for early childhood 
education, a forerunner to Head Start. Hence there was considerable drama 
involved in the effort, as the fate of this national educational initiative rode on 
our work. The algorithms and data being analyzed were sufficiently complex 

that we were not able to anticipate what answers the computer would come up 
with. The answers were, of course, determined by the data, but they were not 
predictable. It turns out that the distinction between being determined and being 
predictable is an important one, to which I will return. 

I remember how exciting it was when the front-panel lights dimmed right 
before the algorithm finished its deliberations, as if the computer were deep in 
thought. When people came by, eager to get the next set of results, I would point 
to the gently flashing lights and say, “It’s thinking.” This both was and wasn’t a 
joke—it really did seem to be contemplating the answers—and staff members 
started to ascribe a personality to the machine. It was an anthropomorphization, 
perhaps, but it did get me to begin to consider in earnest the relationship between 
thinking and computing. 

In order to assess the extent to which my own brain is similar to the 
computer programs I was familiar with, I began to think about what my brain 
must be doing as it processed information. I have continued this investigation for 
fifty years. What I will describe below about our current understanding of how 
the brain works will sound very different from the standard concept of a 
computer. Fundamentally, however, the brain does store and process 
information, and because of the universality of computation—a concept to which 
I will also return—there is more of a parallel between brains and computers than 
may be apparent. 

Each time I do something—or think of something—whether it is brushing 
my teeth, walking across the kitchen, contemplating a business problem, 
practicing on a music keyboard, or coming up with a new idea, I reflect on how I 
was able to accomplish it. I think even more about all of the things that I am not 
able to do, as the limitations of human thought provide an equally important set 
of clues. Thinking so much about thinking might very well be slowing me down, 
but I have been hopeful that such exercises in self-reflection will enable me to 
refine my mental methods. 

To raise our own awareness of how our brains work, let’s consider a series 
of mind experiments. 

Try this: Recite the alphabet. 

You probably remember this from childhood and can do it easily. 

Okay, now try this: Recite the alphabet backward. 

Unless you have studied the alphabet in this order, you are likely to find it 
impossible to do. On occasion someone who has spent a significant amount of 
time in an elementary school classroom where the alphabet is displayed will be 
able to call up his visual memory and then read it backward from that. Even this 
is difficult, though, because we do not actually remember whole images. 

Reciting the alphabet backward should be a simple task, as it involves exactly 
the same information as reciting it forward, yet we are generally unable to do it. 

Do you remember your social security number? If you do, can you recite it 
backward without first writing it down? How about the nursery rhyme “Mary 
Had a Little Lamb”? Computers can do this trivially. Yet we fail at it unless we 
specifically learn the backward sequence as a new series. This tells us something 
important about how human memory is organized. 

Of course, we are able to perform this task easily if we write down the 
sequence and then read it backward. In doing so we are using a technology— 
written language—to compensate for one of the limitations of our unaided 
thinking, albeit a very early tool. (It was our second invention, with spoken 
language as the first.) This is why we invent tools—to compensate for our 

This suggests that our memories are sequential and in order. They can 
be accessed in the order that they are remembered. We are unable to 
directly reverse the sequence of a memory. 

We also have some difficulty starting a memory in the middle of a 
sequence. If I learn to play a piece of music on the piano, I generally can’t just 
begin it at an arbitrary point in its middle. There are a few points at which I can 
jump in, because my sequential memory of the piece is organized in segments. If 
I try to start in the middle of a segment, though, I need to revert to sight-reading 
until my sequential memory kicks in. 

Next, try this: Recall a walk that you took in the last day or so. What do you 
remember about it? 

This mind experiment works best if you took a walk very recently, such as 
earlier today or yesterday. (You can also substitute a drive, or basically any 
activity during which you moved across some terrain.) 

It is likely that you don’t remember much about the experience. Who was 
the fifth person you encountered (not just including people you know)? Did you 
see an oak tree? A mailbox? What did you see when you turned the first corner? 
If you passed some stores, what was in the second window? Perhaps you can 
reconstruct the answers to some of these questions from the few clues that you 
do remember, but it is likely that you remember relatively few details, even 
though this is a very recent experience. 

If you take walks regularly, think back to the first walk you took last month 
(or to the first trip to the office last month, if you commute). You probably 
cannot recall the specific walk or commute at all, and if you do, you doubtless 
recall even fewer details about it than about your walk today. 

I will later discuss the issue of consciousness and make the point that we 

tend to equate consciousness with our memory of events. The primary reason we 
believe that we are not conscious when under anesthesia is that we don’t 
remember anything from that period (albeit there are intriguing—and disturbing 
—exceptions to this). So with regard to the walk I took this morning, was I not 
conscious during most of it? It’s a reasonable question, given that I remember 
almost nothing about what I saw or even what I was thinking about. 

There happen to be a few things I do remember from my walk this morning. 
I recall thinking about this book, but I couldn’t tell you exactly what those 
thoughts were. I also recall passing a woman pushing a baby carriage. I 
remember that the woman was attractive, and that the baby was cute as well. I 
recall two thoughts I had in connection with this experience: This baby is 
adorable, like my new grandson, and What is this baby perceiving in her visual 
surroundings? I cannot recall what either of them was wearing or the color of 
their hair. (My wife will tell you that that is typical.) Although I am unable to 
describe anything specific about their appearance, I do have some ineffable 
sense of what the mom looked like and believe I could pick out her picture from 
among those of several different women. So while there must be something 
about her appearance that I have retained in my memory, if I think about the 
woman, baby carriage, and baby, I am unable to visualize them. There is no 
photograph or video of this event in my mind. It is hard to describe exactly what 
is in my mind about this experience. 

I also recall having passed a different woman with a baby carriage on a 
walk a few weeks earlier. In that case I don’t believe I could even recognize that 
woman’s picture. That memory is now much dimmer than it must have been 
shortly after that walk. 

Next, think about people whom you have encountered only once or twice. 
Can you visualize them clearly? If you are a visual artist, then you may have 
learned this observational skill, but typically we are unable to visualize people 
we’ve only casually come across to draw or describe them sufficiently but would 
have little difficulty in recognizing a picture of them. 

This suggests that there are no images, videos, or sound recordings 
stored in the brain. Our memories are stored as sequences of patterns. 
Memories that are not accessed dim over time. When police sketch artists 
interview a crime victim, they do not ask, “What did the perpetrator’s eyebrows 
look like?” Rather, they will show a series of images of eyebrows and ask the 
victim to select one. The correct set of eyebrows will trigger the recognition of 
the same pattern that is stored in the victim’s memory. 

Let’s now consider faces that you know well. Can you recognize any of 
these people ? 

You are undoubtedly able to recognize these familiar personalities, even 
though they are partially covered or distorted. This represents a key strength of 
human perception: We can recognize a pattern even if only part of it is 
perceived (seen, heard, felt) and even if it contains alterations. Our 
recognition ability is apparently able to detect invariant features of a 
pattern—characteristics that survive real-world variations. The apparent 
distortions in a caricature or in certain forms of art such as impressionism 
emphasize the patterns of an image (person, object) that we recognize while 
changing other details. The world of art is actually ahead of the world of science 
in appreciating the power of the human perceptual system. We use the same 
approach when we recognize a melody from only a few notes. 

Now consider this image: 

The image is ambiguous—the corner indicated by the black region may be 
an inside corner or an outside corner. At first you are likely to perceive it one 
way or the other, though with some effort you can change your perception to the 
alternate interpretation. Once your mind has fixed on an understanding, however, 
it may be difficult to see the other perspective. (This turns out to be true of 
intellectual perspectives as well.) Your brain’s interpretation of the image 

actually influences your experience of it. When the corner appears to be an 
inside one, your brain will interpret the grey region as a shadow, so it does not 
seem to be as dark as when you interpret the corner as being an outside one. 

Thus our conscious experience of our perceptions is actually changed by 
our interpretations. 

Consider that we see what we expect to _ 

I’m confident that you were able to complete the above sentence. 

Had I written out the last word, you would have needed only to glance at it 
momentarily to confirm that it was what you had expected. 

This implies that we are constantly predicting the future and 
hypothesizing what we will experience. This expectation influences what we 
actually perceive. Predicting the future is actually the primary reason that we 
have a brain. 

Consider an experience that we all have on a regular basis: A memory from 
years ago inexplicably pops into your head. 

Often this will be a memory of a person or an event that you haven’t 
thought about for a long time. It is evident that something has triggered the 
memory. The train of thought that did so may be apparent and something you are 
able to articulate. At other times you may be aware of the sequence of thoughts 
that led to the memory but would have a hard time expressing it. Often the 
trigger is quickly lost, so the memory appears to have come from nowhere. I 
often experience these random memories while doing routine procedures such as 
brushing my teeth. Sometimes I may be aware of the connection—the toothpaste 
falling off the toothbrush might remind me of the paint falling off a brush in a 
painting class I took in college. Sometimes I have only a vague sense of the 
connection, or none at all. 

A related phenomenon that everyone experiences frequently is trying to 
think of a name or a word. The procedure we use in this circumstance is to try to 
remind ourselves of triggers that may unlock the memory. (For example: Who 
played Queen Padme in Revenge of the Sith? Let’s see, it’s that same actress 
who was the star in a recent dark movie about dancing, that was Black Swan, oh 
yes, Natalie Portman .) Sometimes we adopt idiosyncratic mnemonics to help us 
remember. (For example: She’s always slim, not portly, oh yes, Portman, Natalie 
Portman .) Some of our memories are sufficiently robust that we can go directly 
from a question (such as who played Queen Padme) to the answer; often we 
need to go through a series of triggers until we find one that works. It’s very 
much like having the right Web link. Memories can indeed become lost like a 
Web page to which no other page links to (at least no page that we can find). 

While executing routine procedures—such as putting on a shirt—watch 

yourself performing them, and consider the extent to which you follow the same 
sequence of steps each time. From my own observation (and as I mentioned, I 
am constantly trying to observe myself), it is likely that you follow very much 
the same steps each time you perform a particular routine task, though there may 
be additional modules added. For example, most of my shirts do not require cuff 
links, but when one does, that involves a further series of tasks. 

The lists of steps in my mind are organized in hierarchies. I follow a routine 
procedure before going to sleep. The first step is to brush my teeth. But this 
action is in turn broken into a smaller series of steps, the first of which is to put 
toothpaste on the toothbrush. That step in turn is made up of yet smaller steps, 
such as finding the toothpaste, removing the cap, and so on. The step of finding 
the toothpaste also has steps, the first of which is to open the bathroom cabinet. 
That step in turn requires steps, the first of which is to grab the outside of the 
cabinet door. This nesting actually continues down to a very fine grain of 
movements, so that there are literally thousands of little actions constituting my 
nighttime routine. Although I may have difficulty remembering details of a walk 
I took just a few hours ago, I have no difficulty recalling all of these many steps 
in preparing for bed—so much so that I am able to think about other things while 
I go through these procedures. It is important to point out that this list is not 
stored as one long list of thousands of steps—rather, each of our routine 
procedures is remembered as an elaborate hierarchy of nested activities. 

The same type of hierarchy is involved in our ability to recognize 
objects and situations. We recognize the faces of people we know well and also 
recognize that these faces contain eyes, a nose, a mouth, and so on—a hierarchy 
of patterns that we use in both our perceptions and our actions. The use of 
hierarchies allows us to reuse patterns. For example, we do not need to relearn 
the concept of a nose and a mouth each time we are introduced to a new face. 

In the next chapter , we’ll put the results of these thought experiments 
together into a theory of how the neocortex must work. I will argue that they 
reveal essential attributes of our thinking that are uniform, from finding the 
toothpaste to writing a poem. 




The brain is a tissue. It is a complicated, intricately woven tissue, like 
nothing else we know of in the universe, but it is composed of cells, as any 
tissue is. They are, to be sure, highly specialized cells, but they function 
according to the laws that govern any other cells. Their electrical and 
chemical signals can be detected, recorded and interpreted and their 
chemicals can be identified; the connections that constitute the brain’s 
woven feltwork can be mapped. In short, the brain can be studied, just as 
the kidney can. 

—David H. Hubei, neuroscientist 

Suppose that there be a machine, the structure of which produces thinking, 
feeling, and perceiving; imagine this machine enlarged but preserving the 
same proportions, so you could enter it as if it were a mill. This being 
supposed, you might visit inside; but what would you observe there? 
Nothing but parts which push and move each other, and never anything that 
could explain perception. 

—Gottfried Wilhelm Leibniz 

A Hierarchy of Patterns 

I have repeated the simple experiments and observations described in the 
previous chapter thousands of times in myriad contexts. The conclusions from 
these observations necessarily constrain my explanation for what the brain must 
be doing, just as the simple experiments on time, space, and mass that were 
conducted in the early and late nineteenth century necessarily constrained the 
young Master Einstein’s reflections on how the universe functioned. In the 
discussion that follows I’ll also factor in some very basic observations from 
neuroscience, attempting to avoid the many details that are still in contention. 

First, let me explain why this section specifically discusses the neocortex 
(from the Latin meaning “new rind”). We do know the neocortex is responsible 
for our ability to deal with patterns of information and to do so in a hierarchical 
fashion. Animals without a neocortex (basically nonmammals) are largely 
incapable of understanding hierarchies.- Understanding and leveraging the 
innately hierarchical nature of reality is a uniquely mammalian trait and results 
from mammals’ unique possession of this evolutionarily recent brain structure. 
The neocortex is responsible for sensory perception, recognition of everything 
from visual objects to abstract concepts, controlling movement, reasoning from 
spatial orientation to rational thought, and language—basically, what we regard 
as “thinking.” 

The human neocortex, the outermost layer of the brain, is a thin, essentially 
two-dimensional structure with a thickness of about 2.5 millimeters (about a 
tenth of an inch). In rodents, it is about the size of a postage stamp and is 
smooth. An evolutionary innovation in primates is that it became intricately 
folded over the top of the rest of the brain with deep ridges, grooves, and 
wrinkles to increase its surface area. Due to its elaborate folding, the neocortex 
constitutes the bulk of the human brain, accounting for 80 percent of its weight. 
Homo sapiens developed a large forehead to allow for an even larger neocortex; 
in particular we have a frontal lobe where we deal with the more abstract 
patterns associated with high-level concepts. 

This thin structure is basically made up of six layers, numbered I (the 
outermost layer) to VI. The axons emerging from the neurons in layers II and III 
project to other parts of the neocortex. The axons (output connections) from 
layers V and VI are connected primarily outside of the neocortex to the 

thalamus, brain stem, and spinal cord. The neurons in layer IV receive synaptic 
(input) connections from neurons that are outside the neocortex, especially in the 
thalamus. The number of layers varies slightly from region to region. Layer IV is 
very thin in the motor cortex, because in that area it largely does not receive 
input from the thalamus, brain stem, or spinal cord. Conversely, in the occipital 
lobe (the part of the neocortex usually responsible for visual processing), there 
are three additional sublayers that can be seen in layer IV, due to the 
considerable input flowing into this region, including from the thalamus. 

A critically important observation about the neocortex is the extraordinary 
uniformity of its fundamental structure. This was first noticed by American 
neuroscientist Vernon Mountcastle (born in 1918). In 1957 Mountcastle 
discovered the columnar organization of the neocortex. In 1978 he made an 
observation that is as significant to neuroscience as the Michelson-Morley ether- 
disproving experiment of 1887 were to physics. That year he described the 
remarkably unvarying organization of the neocortex, hypothesizing that it was 
composed of a single mechanism that was repeated over and over again,- and 
proposing the cortical column as that basic unit. The differences in the height of 
certain layers in different regions noted above are simply differences in the 
amount of interconnectivity that the regions are responsible for dealing with. 

Mountcastle hypothesized the existence of mini-columns within columns, 
but this theory became controversial because there were no visible demarcations 
of such smaller structures. However, extensive experimentation has revealed that 
there are in fact repeating units within the neuron fabric of each column. It is my 
contention that the basic unit is a pattern recognizer and that this constitutes the 
fundamental component of the neocortex. In contrast to Mountcastle’s notion of 
a mini-column, there is no specific physical boundary to these recognizers, as 
they are placed closely one to the next in an interwoven fashion, so the cortical 
column is simply an aggregate of a large number of them. These recognizers are 
capable of wiring themselves to one another throughout the course of a lifetime, 
so the elaborate connectivity (between modules) that we see in the neocortex is 
not prespecified by the genetic code, but rather is created to reflect the patterns 
we actually learn over time. I will describe this thesis in more detail, but I 
maintain that this is how the neocortex must be organized. 

It should be noted, before we further consider the structure of the neocortex, 
that it is important to model systems at the right level. Although chemistry is 
theoretically based on physics and could be derived entirely from physics, this 
would be unwieldy and infeasible in practice, so chemistry has established its 
own rules and models. Similarly, we should be able to deduce the laws of 
thermodynamics from physics, but once we have a sufficient number of particles 

to call them a gas rather than simply a bunch of particles, solving equations for 
the physics of each particle interaction becomes hopeless, whereas the laws of 
thermodynamics work quite well. Biology likewise has its own rules and 
models. A single pancreatic islet cell is enormously complicated, especially if we 
model it at the level of molecules; modeling what a pancreas actually does in 
terms of regulating levels of insulin and digestive enzymes is considerably less 

The same principle applies to the levels of modeling and understanding in 
the brain. It is certainly a useful and necessary part of reverse-engineering the 
brain to model its interactions at the molecular level, but the goal of the effort 
here is essentially to refine our model to account for how the brain processes 
information to produce cognitive meaning. 

American scientist Herbert A. Simon (1916-2001), who is credited with 
cofounding the field of artificial intelligence, wrote eloquently about the issue of 
understanding complex systems at the right level of abstraction. In describing an 
AI program he had devised called EPAM (elementary perceiver and memorizer), 
he wrote in 1973, “Suppose you decided that you wanted to understand the 
mysterious EPAM program that I have. I could provide you with two versions of 
it. One would be...the form in which it was actually written—with its whole 
structure of routines and subroutines.... Alternatively, I could provide you with a 
machine-language version of EPAM after the whole translation had been carried 
out—after it had been flattened so to speak.... I don’t think I need argue at 
length which of these two versions would provide the most parsimonious, the 
most meaningful, the most lawful description.... I will not even propose to you 
the third...of providing you with neither program, but instead with the 
electromagnetic equations and boundary conditions that the computer, viewed as 
a physical system, would have to obey while behaving as EPAM. That would be 
the acme of reduction and incomprehensibility. 

There are about a half million cortical columns in a human neocortex, each 
occupying a space about two millimeters high and a half millimeter wide and 
containing about 60,000 neurons (resulting in a total of about 30 billion neurons 
in the neocortex). A rough estimate is that each pattern recognizer within a 
cortical column contains about 100 neurons, so there are on the order of 300 
million pattern recognizers in total in the neocortex. 

As we consider how these pattern recognizers work, let me begin by saying 
that it is difficult to know precisely where to begin. Everything happens 
simultaneously in the neocortex, so there is no beginning and no end to its 
processes. I will frequently need to refer to phenomena that I have not yet 
explained but plan to come back to, so please bear with these forward references. 

Human beings have only a weak ability to process logic, but a very deep 
core capability of recognizing patterns. To do logical thinking, we need to use 
the neocortex, which is basically a large pattern recognizer. It is not an ideal 
mechanism for performing logical transformations, but it is the only facility we 
have for the job. Compare, for example, how a human plays chess to how a 
typical computer chess program works. Deep Blue, the computer that defeated 
Garry Kasparov, the human world chess champion, in 1997 was capable of 
analyzing the logical implications of 200 million board positions (representing 
different move-countermove sequences) every second. (That can now be done, 
by the way, on a few personal computers.) Kasparov was asked how many 
positions he could analyze each second, and he said it was less than one. How is 
it, then, that he was able to hold up to Deep Blue at all? The answer is the very 
strong ability humans have to recognize patterns. However, we need to train this 
facility, which is why not everyone can play master chess. 

Kasparov had learned about 100,000 board positions. That’s a real number 
—we have established that a human master in a particular field has mastered 
about 100,000 chunks of knowledge. Shakespeare composed his plays with 
100,000 word senses (employing about 29,000 distinct words, but using most of 
them in multiple ways). Medical expert systems that have been built to represent 
the knowledge of a human medical physician have shown that a typical human 
medical specialist has mastered about 100,000 concepts in his or her domain. 
Recognizing a chunk of knowledge from this store is not straightforward, as a 
particular item will present itself a little bit differently each time it is 

Armed with his knowledge, Kasparov looks at the chessboard and 
compares the patterns that he sees to all 100,000 board situations that he has 
mastered, and he does all 100,000 comparisons simultaneously. There is 
consensus on this point: All of our neurons are processing—considering the 
patterns—at the same time. That does not mean that they are all firing 
simultaneously (we would probably fall to the floor if that happened), but while 
doing their processing are considering the possibility of firing. 

How many patterns can the neocortex store? We need to factor in the 
phenomenon of redundancy. The face of a loved one, for example, is not stored 
once but on the order of thousands of times. Some of these repetitions are largely 
the same image of the face, whereas most show different perspectives of it, 
different lighting, different expressions, and so on. None of these repeated 
patterns are stored as images per se (that is, as two-dimensional arrays of pixels). 
Rather, they are stored as lists of features where the constituent elements of a 
pattern are themselves patterns. We’ll describe below more precisely what these 

hierarchies of features look like and how they are organized. 

If we take the core knowledge of an expert as consisting of about 100,000 
“chunks” of knowledge (that is, patterns) with a redundancy estimate of about 
100 to 1, that gives us a requirement of 10 million patterns. This core expert 
knowledge is built on more general and extensive professional knowledge, so we 
can increase the order of magnitude of patterns to about 30 to 50 million. Our 
everyday “commonsense” knowledge as a human being is even greater; “street 
smarts” actually require substantially more of our neocortex than “book smarts.” 
Including this brings our estimate to well over 100 million patterns, taking into 
account the redundancy factor of about 100. Note that the redundancy factor is 
far from fixed—very common patterns will have a redundancy factor well into 
the thousands, whereas a brand-new phenomenon may have a redundancy factor 
of less than 10. 

As I will discuss below, our procedures and actions also comprise patterns 
and are likewise stored in regions of the cortex, so my estimate of the total 
capacity of the human neocortex is on the order of low hundreds of millions of 
patterns. This rough tally correlates well with the number of pattern recognizers 
that I estimated above at about 300 million, so it is a reasonable conclusion that 
the function of each neocortical pattern recognizer is to process one iteration 
(that is, one copy among the multiple redundant copies of most patterns in the 
neocortex) of a pattern. Our estimates of the number of patterns that a human 
brain is capable of dealing with (including necessary redundancy) and the 
number of physical pattern recognizers happen to be the same order of 
magnitude. It should be noted here that when I refer to “processing” a pattern, I 
am referring to all of the things we are able to do with a pattern: learn it, predict 
it (including parts of it), recognize it, and implement it (either by thinking about 
it further or through a pattern of physical movement). 

Three hundred million pattern processors may sound like a large number, 
and indeed it was sufficient to enable Homo sapiens to develop verbal and 
written language, all of our tools, and other diverse creations. These inventions 
have built upon themselves, giving rise to the exponential growth of the 
information content of technologies as described in my law of accelerating 
returns. No other species has achieved this. As I discussed, a few other species, 
such as chimpanzees, do appear to have a rudimentary ability to understand and 
form language and also to use primitive tools. They do, after all, also have a 
neocortex, but their abilities are limited due to its smaller size, especially of the 
frontal lobe. The size of our own neocortex has exceeded a threshold that has 
enabled our species to build ever more powerful tools, including tools that can 
now enable us to understand our own intelligence. Ultimately our brains, 

combined with the technologies they have fostered, will permit us to create a 
synthetic neocortex that will contain well beyond a mere 300 million pattern 
processors. Why not a billion? Or a trillion? 

The Structure of a Pattern 

The pattern recognition theory of mind that I present here is based on the 
recognition of patterns by pattern recognition modules in the neocortex. These 
patterns (and the modules) are organized in hierarchies. I discuss below the 
intellectual roots of this idea, including my own work with hierarchical pattern 
recognition in the 1980s and 1990s and Jeff Hawkins (born in 1957) and Dileep 
George’s (born in 1977) model of the neocortex in the early 2000s. 

Each pattern (which is recognized by one of the estimated 300 million 
pattern recognizers in the neocortex) is composed of three parts. Part one is the 
input, which consists of the lower-level patterns that compose the main pattern. 
The descriptions for each of these lower-level patterns do not need to be repeated 
for each higher-level pattern that references them. For example, many of the 
patterns for words will include the letter “A.” Each of these patterns does not 
need to repeat the description of the letter “A” but will use the same description. 
Think of it as being like a Web pointer. There is one Web page (that is, one 
pattern) for the letter “A,” and all of the Web pages (patterns) for words that 
include “A” will have a link to the “A” page (to the “A” pattern). Instead of Web 
links, the neocortex uses actual neural connections. There is an axon from the 
“A” pattern recognizer that connects to multiple dendrites, one for each word 
that uses “A.” Keep in mind also the redundancy factor: There is more than one 
pattern recognizer for the letter “A.” Any of these multiple “A” pattern 
recognizers can send a signal up to the pattern recognizers that incorporate “A.” 

S' Inhibitory ® Axon ^ 

signals from (output) Pattern expected 

above (signal tram above) 

Size parameter (expected 
size on some dimension 
such as time or distance) for 
this lower-level pattern 

Weight (importance) of 
this lower-level pattern 

Expected variability of 
the size of this 

Each dendrite sending signals into the lower-level pattern 
module represents the presence of a 
lower-level pattern (it also encodes size 
information). A dendrite sending signals out of 
the module indicates that the corresponding 
lower-level pattern is expected. 

The second part of each pattern is the pattern’s name. In the world of 
language, this higher-level pattern is simply the word “apple.” Although we 
directly use our neocortex to understand and process every level of language, 
most of the patterns it contains are not language patterns per se. In the neocortex 
the “name” of a pattern is simply the axon that emerges from each pattern 
processor; when that axon fires, its corresponding pattern has been recognized. 
The firing of the axon is that pattern recognizer shouting the name of the pattern: 
“Hey guys, I just saw the written word ‘apple.’” 

Three redundant (but somewhat different) patterns for “A” feeding up to 
higher-level patterns that incorporate “A.” 

The third and final part of each pattern is the set of higher-level patterns 
that it in turn is part of. For the letter “A,” this is all of the words that include 
“A.” These are, again, like Web links. Each recognized pattern at one level 
triggers the next level that part of that higher-level pattern is present. In the 
neocortex, these links are represented by physical dendrites that flow into 
neurons in each cortical pattern recognizer. Keep in mind that each neuron can 
receive inputs from multiple dendrites yet produces a single output on an axon. 
That axon, however, can then in turn transmit to multiple dendrites. 

To take some simple examples, the simple patterns on the next page are a 
small subset of the patterns used to make up printed letters. Note that every level 
constitutes a pattern. In this case, the shapes are patterns, the letters are patterns, 
and the words are also patterns. Each of these patterns has a set of inputs, a 
process of pattern recognition (based on the inputs that take place in the 
module), and an output (which feeds to the next higher level of pattern 

Southwest to north-central connection: 

Southeast to north-central connection: 

Horizontal crossbar: 

Leftmost vertical line: 

Concave region facing south: 

Bottom horizontal line: 

Top horizontal line: 

Middle horizontal line: 

Loop constituting upper region: 

The above patterns are constituents of the next higher level of pattern, 
which is a category called printed letters (there is no such formal category within 
the neocortex, however; indeed, there are no formal categories). 


Two different patterns, either of which constitutes “A,” and two 
different patterns at a higher level (“APPLE” and “PEAR”) of which “A” is 
a part. 


Patterns that are part of the higher-level pattern “L.” 




▲ ▲ ▲ ▲ 


Patterns that are part of the higher-level pattern “E.” 

These letter patterns feed up to an even higher-level pattern in a category 
called words. (The word “words” is our language category for this concept, but 
the neocortex just treats them only as patterns.) 



tt t t 

Ai p H 


In a different part of the cortex is a comparable hierarchy of pattern 
recognizers processing actual images of objects (as opposed to printed letters). If 
you are looking at an actual apple, low-level recognizers will detect curved 
edges and surface color patterns leading up to a pattern recognizer firing its axon 
and saying in effect, “Hey guys, I just saw an actual apple.” Yet other pattern 
recognizers will detect combinations of frequencies of sound leading up to a 
pattern recognizer in the auditory cortex that might fire its axon indicating, “I 
just heard the spoken word ‘apple.’” 

Keep in mind the redundancy factor—we don’t just have a single pattern 
recognizer for “apple” in each of its forms (written, spoken, visual). There are 
likely to be hundreds of such recognizers firing, if not more. The redundancy not 

only increases the likelihood that you will successfully recognize each instance 
of an apple but also deals with the variations in real-world apples. For apple 
objects, there will be pattern recognizers that deal with the many varied forms of 
apples: different views, colors, shadings, shapes, and varieties. 

Also keep in mind that the hierarchy shown above is a hierarchy of 
concepts. These recognizers are not physically placed above each other; because 
of the thin construction of the neocortex, it is physically only one pattern 
recognizer high. The conceptual hierarchy is created by the connections between 
the individual pattern recognizers. 

An important attribute of the PRTM is how the recognitions are made inside 
each pattern recognition module. Stored in the module is a weight for each input 
dendrite indicating how important that input is to the recognition. The pattern 
recognizer has a threshold for firing (which indicates that this pattern recognizer 
has successfully recognized the pattern it is responsible for). Not every input 
pattern has to be present for a recognizer to fire. The recognizer may still fire if 
an input with a low weight is missing, but it is less likely to fire if a high- 
importance input is missing. When it fires, a pattern recognizer is basically 
saying, “The pattern I am responsible for is probably present.” 

Successful recognition by a module of its pattern goes beyond just counting 
the input signals that are activated (even a count weighted by the importance 
parameter). The size (of each input) matters. There is another parameter (for 
each input) indicating the expected size of the input, and yet another indicating 
how variable that size is. To appreciate how this works, suppose we have a 
pattern recognizer that is responsible for recognizing the spoken word “steep.” 
This spoken word has four sounds: [s], [t], [E], and [p]. The [t] phoneme is what 
is known as a “dental consonant,” meaning that it is created by the tongue 
creating a burst of noise when air breaks its contact with the upper teeth. It is 
essentially impossible to articulate the [t] phoneme slowly. The [p] phoneme is 
considered a “plosive consonant” or “oral occlusive,” meaning that it is created 
when the vocal tract is suddenly blocked (by the lips in the case of [p]) so that 
air no longer passes. It is also necessarily quick. The [E] vowel is caused by 
resonances of the vocal cord and open mouth. It is considered a “long vowel,” 
meaning that it persists for a much longer period of time than consonants such as 
[t] and [p]; however, its duration can be quite variable. The [s] phoneme is 
known as a “sibilant consonant,” and is caused by the passage of air against the 
edges of the teeth, which are held close together. Its duration is typically shorter 
than that of a long vowel such as [E], but it is also variable (in other words, the 
[s] can be said quickly or you can drag it out). 

In our work in speech recognition, we found that it is necessary to encode 

this type of information in order to recognize speech patterns. For example, the 
words “step” and “steep” are very similar. Although the [e] phoneme in “step” 
and the [E] in “steep” are somewhat different vowel sounds (in that they have 
different resonant frequencies), it is not reliable to distinguish these two words 
based on these often confusable vowel sounds. It is much more reliable to 
consider the observation that the [e] in “step” is relatively brief compared with 
the [E] in “steep.” 

We can encode this type of information with two numbers for each input: 
the expected size and the degree of variability of that size. In our “steep” 
example, [t] and [p] would both have a very short expected duration as well as a 
small expected variability (that is, we do not expect to hear long t’s and p’s). The 
[s] sound would have a short expected duration but a larger variability because it 
is possible to drag it out. The [E] sound has a long expected duration as well as a 
high degree of variability. 

In our speech examples, the “size” parameter refers to duration, but time is 
only one possible dimension. In our work in character recognition, we found that 
comparable spatial information was important in order to recognize printed 
letters (for example the dot over the letter “i” is expected to be much smaller 
than the portion under the dot). At much higher levels of abstraction, the 
neocortex will deal with patterns with all sorts of continuums, such as levels of 
attractiveness, irony, happiness, frustration, and myriad others. We can draw 
similarities across rather diverse continuums, as Darwin did when he related the 
physical size of geological canyons to the amount of differentiation among 

In a biological brain, the source of these parameters comes from the brain’s 
own experience. We are not born with an innate knowledge of phonemes; indeed 
different languages have very different sets of them. This implies that multiple 
examples of a pattern are encoded in the learned parameters of each pattern 
recognizer (as it requires multiple instances of a pattern to ascertain the expected 
distribution of magnitudes of the inputs to the pattern). In some AI systems, 
these types of parameters are hand-coded by experts (for example, linguists who 
can tell us the expected durations of different phonemes, as I articulated above). 
In my own work, we found that having an AI system discover these parameters 
on its own from training data (similar to the way the brain does it) was a superior 
approach. Sometimes we used a hybrid approach; that is, we primed the system 
with the intuition of human experts (for the initial settings of the parameters) and 
then had the AI system automatically refine these estimates using a learning 
process from real examples of speech. 

What the pattern recognition module is doing is computing the probability 

(that is, the likelihood based on all of its previous experience) that the pattern 
that it is responsible for recognizing is in fact currently represented by its active 
inputs. Each particular input to the module is active if the corresponding lower- 
level pattern recognizer is firing (meaning that that lower-level pattern was 
recognized). Each input also encodes the observed size (on some appropriate 
dimension such as temporal duration or physical magnitude or some other 
continuum) so that the size can be compared (with the stored size parameters for 
each input) by the module in computing the overall probability of the pattern. 

How does the brain (and how can an AI system) compute the overall 
probability that the pattern (that the module is responsible for recognizing) is 
present given (1) the inputs (each with an observed size), (2) the stored 
parameters on size (the expected size and the variability of size) for each input, 
and (3) the parameters of the importance of each input? In the 1980s and 1990s, 
I and others pioneered a mathematical method called hierarchical hidden Markov 
models for learning these parameters and then using them to recognize 
hierarchical patterns. We used this technique in the recognition of human speech 
as well as the understanding of natural language. I describe this approach further 
in chapter 7 . 

Getting back to the flow of recognition from one level of pattern 
recognizers to the next, in the above example we see the information flow up the 
conceptual hierarchy from basic letter features to letters to words. Recognitions 
will continue to flow up from there to phrases and then more complex language 
structures. If we go up several dozen more levels, we get to higher-level 
concepts like irony and envy. Even though every pattern recognizer is working 
simultaneously, it does take time for recognitions to move upward in this 
conceptual hierarchy. Traversing each level takes between a few hundredths to a 
few tenths of a second to process. Experiments have shown that a moderately 
high-level pattern such as a face takes at least a tenth of a second. It can take as 
long as an entire second if there are significant distortions. If the brain were 
sequential (like conventional computers) and was performing each pattern 
recognition in sequence, it would have to consider every possible low-level 
pattern before moving on to the next level. Thus it would take many millions of 
cycles just to go through each level. That is exactly what happens when we 
simulate these processes on a computer. Keep in mind, however, that computers 
process millions of times faster than our biological circuits. 

A very important point to note here is that information flows down the 
conceptual hierarchy as well as up. If anything, this downward flow is even 
more significant. If, for example, we are reading from left to right and have 
already seen and recognized the letters “A,” “P,” “P,” and “L,” the “APPLE” 

recognizer will predict that it is likely to see an “E” in the next position. It will 
send a signal down to the “E” recognizer saying, in effect, “Please be aware that 
there is a high likelihood that you will see your ‘E’ pattern very soon, so be on 
the lookout for it.” The “E” recognizer then adjusts its threshold such that it is 
more likely to recognize an “E.” So if an image appears next that is vaguely like 
an “E,” but is perhaps smudged such that it would not have been recognized as 
an “E” under “normal” circumstances, the “E” recognizer may nonetheless 
indicate that it has indeed seen an “E,” since it was expected. 

The neocortex is, therefore, predicting what it expects to encounter. 
Envisaging the future is one of the primary reasons we have a neocortex. At the 
highest conceptual level, we are continually making predictions—who is going 
to walk through the door next, what someone is likely to say next, what we 
expect to see when we turn the corner, the likely results of our own actions, and 
so on. These predictions are constantly occurring at every level of the neocortex 
hierarchy. We often misrecognize people and things and words because our 
threshold for confirming an expected pattern is too low. 

In addition to positive signals, there are also negative or inhibitory signals 
which indicate that a certain pattern is less likely to exist. These can come from 
lower conceptual levels (for example, the recognition of a mustache will inhibit 
the likelihood that a person I see in the checkout line is my wife), or from a 
higher level (for example, I know that my wife is on a trip, so the person in the 
checkout line can’t be she). When a pattern recognizer receives an inhibitory 
signal, it raises the recognition threshold, but it is still possible for the pattern to 
fire (so if the person in line really is her, I may still recognize her). 

The Nature of the Data Flowing into a Neocortical Pattern 

Let’s consider further what the data for a pattern looks like. If the pattern is a 
face, the data exists in at least two dimensions. We cannot say that the eyes 
necessarily come first, followed by the nose, and so on. The same thing is true 
for most sounds. A musical piece has at least two dimensions. There may be 
more than one instrument and/or voice making sounds at the same time. 
Moreover, a single note of a complex instrument such as the piano consists of 
multiple frequencies. A single human voice consists of varying levels of energy 
in dozens of different frequency bands simultaneously. So a pattern of sound 
may be complex at any one instant, and these complex instants stretch out over 
time. Tactile inputs are also two-dimensional, since the skin is a two-dimensional 
sense organ, and such patterns may change over the third dimension of time. 

So it would seem that the input to a neocortex pattern processor must 
comprise two- if not three-dimensional patterns. However, we can see in the 
structure of the neocortex that the pattern inputs are only one-dimensional lists. 
All of our work in the field of creating artificial pattern recognition systems 
(such as speech recognition and visual recognition systems) demonstrates that 
we can (and did) represent two- and three-dimensional phenomena with such 
one-dimensional lists. I’ll describe how these methods work in chapter 7 . but for 
now we can proceed with the understanding that the input to each pattern 
processor is a one-dimensional list, even though the pattern itself may inherently 
reflect more than one dimension. 

We should factor in at this point the insight that the patterns we have 
learned to recognize (for example, a specific dog or the general idea of a “dog,” 
a musical note or a piece of music) are exactly the same mechanism that is the 
basis for our memories. Our memories are in fact patterns organized as lists 
(where each item in each list is another pattern in the cortical hierarchy) that we 
have learned and then recognize when presented with the appropriate stimulus. 
In fact, memories exist in the neocortex in order to be recognized. 

The only exception to this is at the lowest possible conceptual level, in 
which the input data to a pattern represents specific sensory information (for 
example, image data from the optic nerve). Even this lowest level of pattern, 
however, has been significantly transformed into simple patterns by the time it 
reaches the cortex. The lists of patterns that constitute a memory are in forward 

order, and we are able to remember our memories only in that order, hence the 
difficulty we have in reversing our memories. 

A memory needs to be triggered by another thought/memory (these are the 
same thing). We can experience this mechanism of triggering when we are 
perceiving a pattern. When we perceived “A,” “P,” “P,” and “L,” the “A P P L E” 
pattern predicted that we would see an “E” and triggered the “E” pattern that it is 
now expected. Our cortex is thereby “thinking” of seeing an “E” even before we 
see it. If this particular interaction in our cortex has our attention, we will think 
about “E” before we see it or even if we never see it. A similar mechanism 
triggers old memories. Usually there is an entire chain of such links. Even if we 
do have some level of awareness of the memories (that is, the patterns) that 
triggered the old memory, memories (patterns) do not have language or image 
labels. This is the reason why old memories may seem to suddenly jump into our 
awareness. Having been buried and not activated for perhaps years, they need a 
trigger in the same way that a Web page needs a Web link to be activated. And 
just as a Web page can become “orphaned” because no other page links to it, the 
same thing can happen to our memories. 

Our thoughts are largely activated in one of two modes, undirected and 
directed, both of which use these same cortical links. In the undirected mode, we 
let the links play themselves out without attempting to move them in any 
particular direction. Some forms of meditation (such as Transcendental 
Meditation, which I practice) are based on letting the mind do exactly this. 
Dreams have this quality as well. 

In directed thinking we attempt to step through a more orderly process of 
recalling a memory (a story, for example) or solving a problem. This also 
involves stepping through lists in our neocortex, but the less structured flurry of 
undirected thought will also accompany the process. The full content of our 
thinking is therefore very disorderly, a phenomenon that James Joyce illuminated 
in his “stream of consciousness” novels. 

As you think through the memories/stories/patterns in your life, whether 
they involve a chance encounter with a mother with a baby carriage and baby on 
a walk or the more important narrative of how you met your spouse, your 
memories consist of a sequence of patterns. Because these patterns are not 
labeled with words or sounds or pictures or videos, when you try to recall a 
significant event, you will essentially be reconstructing the images in your mind, 
because the actual images do not exist. 

If we were to “read” the mind of someone and peer at exactly what is going 
on in her neocortex, it would be very difficult to interpret her memories, whether 
we were to take a look at patterns that are simply stored in the neocortex waiting 

to be triggered or those that have been triggered and are currently being 
experienced as active thoughts. What we would “see” is the simultaneous 
activation of millions of pattern recognizers. A hundredth of a second later, we 
would see a different set of a comparable number of activated pattern 
recognizers. Each such pattern would be a list of other patterns, and each of 
those patterns would be a list of other patterns, and so on until we reached the 
most elementary simple patterns at the lowest level. It would be extremely 
difficult to interpret what these higher-level patterns meant without actually 
copying all of the information at every level into our own cortex. Thus each 
pattern in our neocortex is meaningful only in light of all the information carried 
in the levels below it. Moreover, other patterns at the same level and at higher 
levels are also relevant in interpreting a particular pattern because they provide 
context. True mind reading, therefore, would necessitate not just detecting the 
activations of the relevant axons in a person’s brain, but examining essentially 
her entire neocortex with all of its memories to understand these activations. 

As we experience our own thoughts and memories, we “know” what they 
mean, but they do not exist as readily explainable thoughts and recollections. If 
we want to share them with others, we need to translate them into language. This 
task is also accomplished by the neocortex, using pattern recognizers trained 
with patterns that we have learned for the purpose of using language. Language 
is itself highly hierarchical and evolved to take advantage of the hierarchical 
nature of the neocortex, which in turn reflects the hierarchical nature of reality. 
The innate ability of humans to learn the hierarchical structures in language that 
Noam Chomsky wrote about reflects the structure of the neocortex. In a 2002 
paper he coauthored, Chomsky cites the attribute of “recursion” as accounting 
for the unique language faculty of the human species;- Recursion, according to 
Chomsky, is the ability to put together small parts into a larger chunk, and then 
use that chunk as a part in yet another structure, and to continue this process 
iteratively. In this way we are able to build the elaborate structures of sentences 
and paragraphs from a limited set of words. Although Chomsky was not 
explicitly referring here to brain structure, the capability he is describing is 
exactly what the neocortex does. 

Lower species of mammals largely use up their neocortex with the 
challenges of their particular lifestyles. The human species acquired additional 
capacities by having grown substantially more cortex to handle spoken and 
written language. Some people have learned such skills better than others. If we 
have told a particular story many times, we will begin to actually learn the 
sequence of language that describes the story as a series of separate sequences. 
Even in this case our memory is not a strict sequence of words, but rather of 

language structures that we need to translate into specific word sequences each 
time we deliver the story. That is why we tell a story a bit differently each time 
we share it (unless we learn the exact word sequence as a pattern). 

For each of these descriptions of specific thought processes, we also need to 
consider the issue of redundancy. As I mentioned, we don’t have a single pattern 
representing the important entities in our lives, whether those entities constitute 
sensory categories, language concepts, or memories of events. Every important 
pattern—at every level—is repeated many times. Some of these recurrences 
represent simple repetitions, whereas many represent different perspectives and 
vantage points. This is a principal reason why we can recognize a familiar face 
from various orientations and under a range of lighting conditions. Each level up 
the hierarchy has substantial redundancy, allowing sufficient variability that is 
consistent with that concept. 

So if we were to imagine examining your neocortex when you were looking 
at a particular loved one, we would see a great many firings of the axons of the 
pattern recognizers at every level, from the basic level of primitive sensory 
patterns up to many different patterns representing that loved one’s image. We 
would also see massive numbers of firings representing other aspects of the 
situation, such as that person’s movements, what she is saying, and so on. So if 
the experience seems much richer than just an orderly trip up a hierarchy of 
features, it is. 

A computer simulation of the firings of many simultaneous pattern 

recognizers in the neocortex. 

But the basic mechanism of going up a hierarchy of pattern recognizers in 
which each higher conceptual level represents a more abstract and more 
integrated concept remains valid. The flow of information downward is even 
greater, as each activated level of recognized pattern sends predictions to the 
next lower-level pattern recognizer of what it is likely to be encountering next. 
The apparent lushness of human experience is a result of the fact that all of the 
hundreds of millions of pattern recognizers in our neocortex are considering 
their inputs simultaneously. 

In chapter 5 I’ll discuss the flow of information from touch, vision, hearing, 
and other sensory organs into the neocortex. These early inputs are processed by 
cortical regions that are devoted to relevant types of sensory input (although 
there is enormous plasticity in the assignment of these regions, reflecting the 
basic uniformity of function in the neocortex). The conceptual hierarchy 
continues above the highest concepts in each sensory region of the neocortex. 
The cortical association areas integrate input from the different sensory inputs. 
When we hear something that perhaps sounds like our spouse’s voice, and then 
see something that is perhaps indicative of her presence, we don’t engage in an 
elaborate process of logical deduction; rather, we instantly perceive that our 
spouse is present from the combination of these sensory recognitions. We 
integrate all of the germane sensory and perceptual cues—perhaps even the 
smell of her perfume or his cologne—as one multilevel perception. 

At a conceptual level above the cortical sensory association areas, we are 
capable of dealing with—perceiving, remembering, and thinking about—even 
more abstract concepts. At the highest level we recognize patterns such as that’s 
funny, or she’s pretty, or that’s ironic, and so on. Our memories include these 
abstract recognition patterns as well. For example, we might recall that we were 
taking a walk with someone and that she said something funny, and we laughed, 
though we may not remember the actual joke itself. The memory sequence for 
that recollection has simply recorded the perception of humor but not the precise 
content of what was funny. 

In the previous chapter I noted that we can often recognize a pattern even 
though we don’t recognize it well enough to be able to describe it. For example, 
I believe I could pick out a picture of the woman with the baby carriage whom I 
saw earlier today from among a group of pictures of other women, despite the 
fact that I am unable to actually visualize her and cannot describe much specific 
about her. In this case my memory of her is a list of certain high-level features. 
These features do not have language or image labels attached to them, and they 
are not pixel images, so while I am able to think about her, I am unable to 

describe her. However, if I am presented with a picture of her, I can process the 
image, which results in the recognition of the same high-level features that were 
recognized the first time I saw her. I would be able to thereby determine that the 
features match and thus confidently pick out her picture. 

Even though I saw this woman only once on my walk, there are probably 
already multiple copies of her pattern in my neocortex. However, if I don’t think 
about her for a given period of time, then these pattern recognizers will become 
reassigned to other patterns. That is why memories grow dimmer with time: The 
amount of redundancy becomes reduced until certain memories become extinct. 
However, now that I have memorialized this particular woman by writing about 
her here, I probably won’t forget her so easily. 

Autoassociation and Invariance 

In the previous chapter I discussed how we can recognize a pattern even if the 
entire pattern is not present, and also if it is distorted. The first capability is 
called autoassociation: the ability to associate a pattern with a part of itself. The 
structure of each pattern recognizer inherently supports this capability. 

As each input from a lower-level pattern recognizer flows up to a higher- 
level one, the connection can have a “weight,” indicating how important that 
particular element in the pattern is. Thus the more significant elements of a 
pattern are more heavily weighted in considering whether that pattern should 
trigger as “recognized.” Lincoln’s beard, Elvis’s sideburns, and Einstein’s 
famous tongue gesture are likely to have high weights in the patterns we’ve 
learned about the appearance of these iconic figures. The pattern recognizer 
computes a probability that takes the importance parameters into account. Thus 
the overall probability is lower if one or more of the elements is missing, though 
the threshold of recognition may nonetheless be met. As I pointed out, the 
computation of the overall probability (that the pattern is present) is more 
complicated than a simple weighted sum in that the size parameters also need to 
be considered. 

If the pattern recognizer has received a signal from a higher-level 
recognizer that its pattern is “expected,” then the threshold is effectively lowered 
(that is, made easier to achieve). Alternatively, such a signal may simply add to 
the total of the weighted inputs, thereby compensating for a missing element. 
This happens at every level, so that a pattern such as a face that is several levels 
up from the bottom may be recognized even with multiple missing features. 

The ability to recognize patterns even when aspects of them are 
transformed is called feature invariance, and is dealt with in four ways. First, 
there are global transformations that are accomplished before the neocortex 
receives sensory data. We will discuss the voyage of sensory data from the eyes, 
ears, and skin in the section “The Sensory Pathway” on page 94 . 

The second method takes advantage of the redundancy in our cortical 
pattern memory. Especially for important items, we have learned many different 
perspectives and vantage points for each pattern. Thus many variations are 
separately stored and processed. 

The third and most powerful method is the ability to combine two lists. One 
list can have a set of transformations that we have learned may apply to a certain 

category of pattern; the cortex will apply this same list of possible changes to 
another pattern. That is how we understand such language phenomena as 
metaphors and similes. 

For example, we have learned that certain phonemes (the basic sounds of 
language) may be missing in spoken speech (for example, “gom”’). If we then 
learn a new spoken word (for example, “driving”), we will be able to recognize 
that word if one of its phonemes is missing even if we have never experienced 
that word in that form before, because we have become familiar with the general 
phenomenon of certain phonemes being omitted. As another example, we may 
learn that a particular artist likes to emphasize (by making larger) certain 
elements of a face, such as the nose. We can then identify a face with which we 
are familiar to which that modification has been applied even if we have never 
seen that modification on that face. Certain artistic modifications emphasize the 
very features that are recognized by our pattern recognition-based neocortex. As 
mentioned, that is precisely the basis of caricature. 

The fourth method derives from the size parameters that allow a single 
module to encode multiple instances of a pattern. For example, we have heard 
the word “steep” many times. A particular pattern recognition module that is 
recognizing this spoken word can encode these multiple examples by indicating 
that the duration of [E] has a high expected variability. If all the modules for 
words including [E] share a similar phenomenon, that variability could be 
encoded in the models for [E] itself. However, different words incorporating [E] 
(or many other phonemes) may have different amounts of expected variability. 
For example, the word “peak” is likely not to have the [E] phoneme as drawn out 
as in the word “steep.” 


Are we not ourselves creating our successors in the supremacy of the earth? 
Daily adding to the beauty and delicacy of their organization, daily giving 
them greater skill and supplying more and more of that self-regulating self¬ 
acting power which will be better than any intellect? 

—Samuel Butler, 1871 

The principal activities of brains are making changes in themselves. 

—Marvin Minsky, The Society of Mind 

So far we have examined how we recognize (sensory and perceptual) patterns 
and recall sequences of patterns (our memory of things, people, and events). 
However, we are not born with a neocortex filled with any of these patterns. Our 
neocortex is virgin territory when our brain is created. It has the capability of 
learning and therefore of creating connections between its pattern recognizers, 
but it gains those connections from experience. 

This learning process begins even before we are born, occurring 
simultaneously with the biological process of actually growing a brain. A fetus 
already has a brain at one month, although it is essentially a reptile brain, as the 
fetus actually goes through a high-speed re-creation of biological evolution in 
the womb. The natal brain is distinctly a human brain with a human neocortex 
by the time it reaches the third trimester of pregnancy. At this time the fetus is 
having experiences, and the neocortex is learning. She can hear sounds, 
especially her mother’s heartbeat, which is one likely reason that the rhythmic 
qualities of music are universal to human culture. Every human civilization ever 
discovered has had music as part of its culture, which is not the case with other 
art forms, such as pictorial art. It is also the case that the beat of music is 
comparable to our heart rate. Music beats certainly vary—otherwise music 
would not keep our interest—but heartbeats vary also. An overly regular 
heartbeat is actually a symptom of a diseased heart. The eyes of a fetus are 
partially open twenty-six weeks after conception, and are fully open most of the 
time by twenty-eight weeks after conception. There may not be much to see 
inside the womb, but there are patterns of light and dark that the neocortex 

begins to process. 

So while a newborn baby has had a bit of experience in the womb, it is 
clearly limited. The neocortex may also learn from the old brain (a topic I 
discuss in chapter 5 ). but in general at birth the child has a lot to learn— 
everything from basic primitive sounds and shapes to metaphors and sarcasm. 

Learning is critical to human intelligence. If we were to perfectly model 
and simulate the human neocortex (as the Blue Brain Project is attempting to do) 
and all of the other brain regions that it requires to function (such as the 
hippocampus and thalamus), it would not be able to do very much—in the same 
way that a newborn infant cannot do much (other than to be cute, which is 
definitely a key survival adaptation). 

Learning and recognition take place simultaneously. We start learning 
immediately, and as soon as we’ve learned a pattern, we immediately start 
recognizing it. The neocortex is continually trying to make sense of the input 
presented to it. If a particular level is unable to fully process and recognize a 
pattern, it gets sent to the next higher level. If none of the levels succeeds in 
recognizing a pattern, it is deemed to be a new pattern. Classifying a pattern as 
new does not necessarily mean that every aspect of it is new. If we are looking at 
the paintings of a particular artist and see a cat’s face with the nose of an 
elephant, we will be able to identify each of the distinctive features but will 
notice that this combined pattern is something novel, and are likely to remember 
it. Higher conceptual levels of the neocortex, which understand context—for 
example, the circumstance that this picture is an example of a particular artist’s 
work and that we are attending an opening of a showing of new paintings by that 
artist—will note the unusual combination of patterns in the cat-elephant face but 
will also include these contextual details as additional memory patterns. 

New memories such as the cat-elephant face are stored in an available 
pattern recognizer. The hippocampus plays a role in this process, and we’ll 
discuss what is known about the actual biological mechanisms in the following 
chapter. For the purposes of our neocortex model, it is sufficient to say that 
patterns that are not otherwise recognized are stored as new patterns and are 
appropriately connected to the lower-level patterns that form them. The cat- 
elephant face, for example, will be stored in several different ways: The novel 
arrangement of facial parts will be stored as well as contextual memories that 
include the artist, the situation, and perhaps the fact that we laughed when we 
first saw it. 

Memories that are successfully recognized may also result in the creation of 
a new pattern to achieve greater redundancy. If patterns are not perfectly 
recognized, they are likely to be stored as reflecting a different perspective of the 

item that was recognized. 

What, then, is the overall method for determining what patterns get stored? 
In mathematical terms, the problem can be stated as follows: Using the available 
limits of pattern storage, how do we optimally represent the input patterns that 
have thus far been presented? While it makes sense to allow for a certain amount 
of redundancy, it would not be practical to fill up the entire available storage area 
(that is, the entire neocortex) with repeated patterns, as that would not allow for 
a sufficient diversity of patterns. A pattern such as the [E] phoneme in spoken 
words is something we have experienced countless times. It is a simple pattern 
of sound frequencies and it undoubtedly enjoys significant redundancy in our 
neocortex. We could fill up our entire neocortex with repeated patterns of the [E] 
phoneme. There is a limit, however, to useful redundancy, and a common pattern 
such as this clearly has reached it. 

There is a mathematical solution to this optimization problem called linear 
programming, which solves for the best possible allocation of limited resources 
(in this case, a limited number of pattern recognizers) that would represent all of 
the cases on which the system has trained. Linear programming is designed for 
systems with one-dimensional inputs, which is another reason why it is optimal 
to represent the input to each pattern recognition module as a linear string of 
inputs. We can use this mathematical approach in a software system, and though 
an actual brain is further constrained by the physical connections it has available 
that it can adapt between pattern recognizers, the method is nonetheless similar. 

An important implication of this optimal solution is that experiences that 
are routine are recognized but do not result in a permanent memory’s being 
made. With regard to my walk, I experienced millions of patterns at every level, 
from basic visual edges and shadings to objects such as lampposts and mailboxes 
and people and animals and plants that I passed. Almost none of what I 
experienced was unique, and the patterns that I recognized had long since 
reached their optimal level of redundancy. The result is that I recall almost 
nothing from this walk. The few details that I do remember are likely to get 
overwritten with new patterns by the time I take another few dozen walks— 
except for the fact that I have now memorialized this particular walk by writing 
about it. 

One important point that applies to both our biological neocortex and 
attempts to emulate it is that it is difficult to learn too many conceptual levels 
simultaneously. We can essentially learn one or at most two conceptual levels at 
a time. Once that learning is relatively stable, we can go on to learn the next 
level. We may continue to fine-tune the learning in the lower levels, but our 
learning focus is on the next level of abstraction. This is true at both the 

beginning of life, as newborns struggle with basic shapes, and later in life, as we 
struggle to learn new subject matter, one level of complexity at a time. We find 
the same phenomenon in machine emulations of the neocortex. However, if they 
are presented increasingly abstract material one level at a time, machines are 
capable of learning just as humans do (although not yet with as many conceptual 

The output of a pattern can feed back to a pattern at a lower level or even to 
the pattern itself, giving the human brain its powerful recursive ability. An 
element of a pattern can be a decision point based on another pattern. This is 
especially useful for lists that compose actions—for example, getting another 
tube of toothpaste if the current one is empty. These conditionals exist at every 
level. As anyone who has attempted to program a procedure on a computer 
knows, conditionals are vital to describing a course of action. 

The Language of Thought 

The dream acts as a safety-valve for the over-burdened brain. 

—Sigmund Freud, 

The Interpretation of Dreams, 1911 

Brain: an apparatus with which we think we think. 

—Ambrose Bierce, The Devil’s Dictionary 

To summarize what we’ve learned so far about the way the neocortex works, 
please refer to the diagram of the neocortical pattern recognition module on page 


a) Dendrites enter the module that represents the pattern. Even though 
patterns may seem to have two- or three-dimensional qualities, they are 
represented by a one-dimensional sequence of signals. The pattern must 
be present in this (sequential) order for the pattern recognizer to be able 
to recognize it. Each of the dendrites is connected ultimately to one or 
more axons of pattern recognizers at a lower conceptual level that have 
recognized a lower-level pattern that constitutes part of this pattern. For 
each of these input patterns, there may be many lower-level pattern 
recognizers that can generate the signal that the lower-level pattern has 
been recognized. The necessary threshold to recognize the pattern may 
be achieved even if not all of the inputs have signaled. The module 
computes the probability that the pattern it is responsible for is present. 
This computation considers the “importance” and “size” parameters (see 
[f] below). 

Note that some of the dendrites transmit signals into the module and 
some out of the module. If all of the input dendrites to this pattern 
recognizer are signaling that their lower-level patterns have been 
recognized except for one or two, then this pattern recognizer will send a 
signal down to the pattern recognizer(s) recognizing the lower-level 
patterns that have not yet been recognized, indicating that there is a high 
likelihood that that pattern will soon be recognized and that lower-level 

recognizer(s) should be on the lookout for it. 

b) When this pattern recognizer recognizes its pattern (based on all or most 
of the input dendrite signals being activated), the axon (output) of this 
pattern recognizer will activate. In turn, this axon can connect to an entire 
network of dendrites connecting to many higher-level pattern recognizers 
that this pattern is input to. This signal will transmit magnitude 
information so that the pattern recognizers at the next higher conceptual 
level can consider it. 

c) If a higher-level pattern recognizer is receiving a positive signal from all 
or most of its constituent patterns except for the one represented by this 
pattern recognizer, then that higher-level recognizer might send a signal 
down to this recognizer indicating that its pattern is expected. Such a 
signal would cause this pattern recognizer to lower its threshold, meaning 
that it would be more likely to send a signal on its axon (indicating that 
its pattern is considered to have been recognized) even if some of its 
inputs are missing or unclear. 

d) Inhibitory signals from below would make it less likely that this pattern 
recognizer will recognize its pattern. This can result from recognition of 
lower-level patterns that are inconsistent with the pattern associated with 
this pattern recognizer (for example, recognition of a mustache by a 
lower-level recognizer would make it less likely that this image is “my 

e) Inhibitory signals from above would also make it less likely that this 
pattern recognizer will recognize its pattern. This can result from a 
higher-level context that is inconsistent with the pattern associated with 
this recognizer. 

f) For each input, there are stored parameters for importance, expected size, 
and expected variability of size. The module computes an overall 
probability that the pattern is present based on all of these parameters and 
the current signals indicating which of the inputs are present and their 
magnitudes. A mathematically optimal way to accomplish this is with a 
technique called hidden Markov models. When such models are 
organized in a hierarchy (as they are in the neocortex or in attempts to 
simulate a neocortex), we call them hierarchical hidden Markov models. 

Patterns triggered in the neocortex trigger other patterns. Partially complete 
patterns send signals down the conceptual hierarchy; completed patterns send 
signals up the conceptual hierarchy. These neocortical patterns are the language 
of thought. Just like language, they are hierarchical, but they are not language 
per se. Our thoughts are not conceived primarily in the elements of language, 
although since language also exists as hierarchies of patterns in our neocortex, 
we can have language-based thoughts. But for the most part, thoughts are 
represented in these neocortical patterns. 

As I discussed above, if we were able to detect the pattern activations in 
someone’s neocortex, we would still have little idea what those pattern 
activations meant without also having access to the entire hierarchy of patterns 
above and below each activated pattern. That would pretty much require access 
to that person’s entire neocortex. It is hard enough for us to understand the 
content of our own thoughts, but understanding another person’s requires 
mastering a neocortex different from our own. Of course we don’t yet have 
access to someone else’s neocortex; we need instead to rely on her attempts to 
express her thoughts into language (as well as other means such as gestures). 
People’s incomplete ability to accomplish these communication tasks adds 
another layer of complexity—it is no wonder that we misunderstand one another 
as much as we do. 

We have two modes of thinking. One is nondirected thinking, in which 
thoughts trigger one another in a nonlogical way. When we experience a sudden 
recollection of a memory from years or decades ago while doing something else, 
such as raking the leaves or walking down the street, the experience is recalled— 
as all memories are—as a sequence of patterns. We do not immediately visualize 
the scene unless we can call upon a lot of other memories that enable us to 
synthesize a more robust recollection. If we do visualize the scene in that way, 
we are essentially creating it in our mind from hints at the time of recollection; 
the memory itself is not stored in the form of images or visualizations. As I 
mentioned earlier, the triggers that led this thought to pop into our mind may or 
may not be evident. The sequence of relevant thoughts may have been 
immediately forgotten. Even if we do remember it, it will be a nonlinear and 
circuitous sequence of associations. 

The second mode of thinking is directed thinking, which we use when we 
attempt to solve a problem or formulate an organized response. For example, we 
might be rehearsing in our mind something we plan to say to someone, or we 
might be formulating a passage we want to write (in a book on the mind, 
perhaps). As we think about tasks such as these, we have already broken down 

each one into a hierarchy of subtasks. Writing a book, for example, involves 
writing chapters; each chapter has sections; each section has paragraphs; each 
paragraph contains sentences that express ideas; each idea has its configuration 
of elements; each element and each relationship between elements is an idea that 
needs to be articulated; and so on. At the same time, our neocortical structures 
have learned certain rules that should be followed. If the task is writing, then we 
should try to avoid unnecessary repetition; we should try to make sure that the 
reader can follow what is being written; we should try to follow rules about 
grammar and style; and so on. The writer needs therefore to build a model of the 
reader in his mind, and that construct is hierarchical as well. In doing directed 
thinking, we are stepping through lists in our neocortex, each of which expands 
into extensive hierarchies of sublists, each with its own considerations. Keep in 
mind that elements in a list in a neocortical pattern can include conditionals, so 
our subsequent thoughts and actions will depend on assessments made as we go 
through the process. 

Moreover, each such directed thought will trigger hierarchies of undirected 
thoughts. A continual storm of ruminations attends both our sensory experiences 
and our attempts at directed thinking. Our actual mental experience is complex 
and messy, made up of these lightning storms of triggered patterns, which 
change about a hundred times a second. 

The Language of Dreams 

Dreams are examples of undirected thoughts. They make a certain amount of 
sense because the phenomenon of one thought’s triggering another is based on 
the actual linkages of patterns in our neocortex. To the extent that a dream does 
not make sense, we attempt to fix it through our ability to confabulate. As I will 
describe in chapter 9 . split-brain patients (whose corpus callosum, which 
connects the two hemispheres of the brain, is severed or damaged) will 
confabulate (make up) explanations with their left brain—which controls the 
speech center—to explain what the right brain just did with input that the left 
brain did not have access to. We confabulate all the time in explaining the 
outcome of events. If you want a good example of this, just tune in to the daily 
commentary on the movement of financial markets. No matter how the markets 
perform, it’s always possible to come up with a good explanation for why it 
happened, and such after-the-fact commentary is plentiful. Of course, if these 
commentators really understood the markets, they wouldn’t have to waste their 
time doing commentary. 

The act of confabulating is of course also done in the neocortex, which is 
good at coming up with stories and explanations that meet certain constraints. 
We do that whenever we retell a story. We will fill in details that may not be 
available or that we may have forgotten so that the story makes more sense. That 
is why stories change over time as they are told over and over again by new 
storytellers with perhaps different agendas. As spoken language led to written 
language, however, we had a technology that could record a definitive version of 
a story and prevent this sort of drift. 

The actual content of a dream, to the extent that we remember it, is again a 
sequence of patterns. These patterns represent constraints in a story; we then 
confabulate a story that fits these constraints. The version of the dream that we 
retell (even if only to ourselves silently) is this confabulation. As we recount a 
dream we trigger cascades of patterns that fill in the actual dream as we 
originally experienced it. 

There is one key difference between dream thoughts and our thinking while 
awake. One of the lessons we learn in life is that certain actions, even thoughts, 
are not permissible in the real world. For example, we learn that we cannot 
immediately fulfill our desires. There are rules against grabbing the money in the 
cash register at a store, and constraints on interacting with a person to whom we 

may be physically attracted. We also learn that certain thoughts are not 
permissible because they are culturally forbidden. As we learn professional 
skills, we learn the ways of thinking that are recognized and rewarded in our 
professions, and thereby avoid patterns of thought that might betray the methods 
and norms of that profession. Many of these taboos are worthwhile, as they 
enforce social order and consolidate progress. However, they can also prevent 
progress by enforcing an unproductive orthodoxy. Such orthodoxy is precisely 
what Einstein left behind when he tried to ride a light beam with his thought 

Cultural rules are enforced in the neocortex with help from the old brain, 
especially the amygdala. Every thought we have triggers other thoughts, and 
some of them will relate to associated dangers. We learn, for example, that 
breaking a cultural norm even in our private thoughts can lead to ostracism, 
which the neocortex realizes threatens our well-being. If we entertain such 
thoughts, the amygdala is triggered, and that generates fear, which generally 
leads to terminating that thought. 

In dreams, however, these taboos are relaxed, and we will often dream 
about matters that are culturally, sexually, or professionally forbidden. It is as if 
our brain realizes that we are not an actual actor in the world while dreaming. 
Freud wrote about this phenomenon but also noted that we will disguise such 
dangerous thoughts, at least when we attempt to recall them, so that the awake 
brain continues to be protected from them. 

Relaxing professional taboos turns out to be useful for creative problem 
solving. I use a mental technique each night in which I think about a particular 
problem before I go to sleep. This triggers sequences of thoughts that will 
continue into my dreams. Once I am dreaming, I can think— dream —about 
solutions to the problem without the burden of the professional restraints I carry 
during the day. I can then access these dream thoughts in the morning while in 
an in-between state of dreaming and being awake, sometimes referred to as 
“lucid dreaming. 

Freud also famously wrote about the ability to gain insight into a person’s 
psychology by interpreting dreams. There is of course a vast literature on all 
aspects of this theory, but the fundamental notion of gaining insight into 
ourselves through examination of our dreams makes sense. Our dreams are 
created by our neocortex, and thus their substance can be revealing of the 
content and connections found there. The relaxation of the constraints on our 
thinking that exist while we are awake is also useful in revealing neocortical 
content that we otherwise would be unable to access directly. It is also 
reasonable to conclude that the patterns that end up in our dreams represent 

important matters to us and thereby clues in understanding our unresolved 
desires and fears. 

The Roots of the Model 

As I mentioned above, I led a team in the 1980s and 1990s that developed the 
technique of hierarchical hidden Markov models to recognize human speech and 
understand natural-language statements. This work was the predecessor to 
today’s widespread commercial systems that recognize and understand what we 
are trying to tell them (car navigation systems that you can talk to, Siri on the 
iPhone, Google Voice Search, and many others). The technique we developed 
had substantially all of the attributes that I describe in the PRTM. It included a 
hierarchy of patterns with each higher level being conceptually more abstract 
than the one below it. For example, in speech recognition the levels included 
basic patterns of sound frequency at the lowest level, then phonemes, then words 
and phrases (which were often recognized as if they were words). Some of our 
speech recognition systems could understand the meaning of natural-language 
commands, so yet higher levels included such structures as noun and verb 
phrases. Each pattern recognition module could recognize a linear sequence of 
patterns from a lower conceptual level. Each input had parameters for 
importance, size, and variability of size. There were “downward” signals 
indicating that a lower-level pattern was expected. I discuss this research in more 
detail in chapter 7 . 

In 2003 and 2004, PalmPilot inventor Jeff Hawkins and Dileep George 
developed a hierarchical cortical model called hierarchical temporal memory. 
With science writer Sandra Blakeslee, Hawkins described this model eloquently 
in their book On Intelligence. Hawkins provides a strong case for the uniformity 
of the cortical algorithm and its hierarchical and list-based organization. There 
are some important differences between the model presented in On Intelligence 
and what I present in this book. As the name implies, Hawkins is emphasizing 
the temporal (time-based) nature of the constituent lists. In other words, the 
direction of the lists is always forward in time. His explanation for how the 
features in a two-dimensional pattern such as the printed letter “A” have a 
direction in time is predicated on eye movement. He explains that we visualize 
images using saccades, which are very rapid movements of the eye of which we 
are unaware. The information reaching the neocortex is therefore not a two- 
dimensional set of features but rather a time-ordered list. While it is true that our 
eyes do make very rapid movements, the sequence in which they view the 
features of a pattern such as the letter “A” does not always occur in a consistent 

temporal order. (For example, eye saccades will not always register the top 
vertex in “A” before its bottom concavity.) Moreover, we can recognize a visual 
pattern that is presented for only a few tens of milliseconds, which is too short a 
period of time for eye saccades to scan it. It is true that the pattern recognizers in 
the neocortex store a pattern as a list and that the list is indeed ordered, but the 
order does not necessarily represent time. That is often indeed the case, but it 
may also represent a spatial or higher-level conceptual ordering as I discussed 

The most important difference is the set of parameters that I have included 
for each input into the pattern recognition module, especially the size and size 
variability parameters. In the 1980s we actually tried to recognize human speech 
without this type of information. This was motivated by linguists’ telling us that 
the duration information was not especially important. This perspective is 
illustrated by dictionaries that write out the pronunciation of each word as a 
string of phonemes, for example the word “steep” as [s] [t] [E] [p], with no 
indication of how long each phoneme is expected to last. The implication is that 
if we create programs to recognize phonemes and then encounter this particular 
sequence of four phonemes (in a spoken utterance), we should be able to 
recognize that spoken word. The system we built using this approach worked to 
some extent but not well enough to deal with such attributes as a large 
vocabulary, multiple speakers, and words spoken continuously without pauses. 
When we used the technique of hierarchical hidden Markov models in order to 
incorporate the distribution of magnitudes of each input, performance soared. 



Because important things go in a case, you’ve got a skull for your brain, a 
plastic sleeve for your comb, and a wallet for your money. 

—George Costanza, in “The Reverse Peephole” episode of Seinfeld 

Now, for the first time, we are observing the brain at work in a global 
manner with such clarity that we should be able to discover the overall 
programs behind its magnificent powers. 

—J. G. Taylor, B. Horwitz, and K. J. Friston 

The mind, in short, works on the data it receives very much as a sculptor 
works on his block of stone. In a sense the statue stood there from eternity. 
But there were a thousand different ones beside it, and the sculptor alone is 
to thank for having extricated this one from the rest. Just so the world of 
each of us, howsoever different our several views of it may be, all lay 
embedded in the primordial chaos of sensations, which gave the mere 
matter to the thought of all of us indifferently. We may, if we like, by our 
reasonings unwind things back to that black and jointless continuity of 
space and moving clouds of swarming atoms which science calls the only 
real world. But all the while the world we feel and live in will be that which 
our ancestors and we, by slowly cumulative strokes of choice, have 
extricated out of this, like sculptors, by simply rejecting certain portions of 
the given stuff. Other sculptors, other statues from the same stone! Other 
minds, other worlds from the same monotonous and inexpressive chaos! 
My world is but one in a million alike embedded, alike real to those who 
may abstract them. How different must be the worlds in the consciousness 
of ant, cuttle-fish, or crab! 

—William James 

Is intelligence the goal, or even a goal, of biological evolution? Steven Pinker 
writes, “We are chauvinistic about our brains, thinking them to be the goal of 
evolution,”- and goes on to argue that “that makes no sense.... Natural selection 
does nothing even close to striving for intelligence. The process is driven by 
differences in the survival and reproduction rates of replicating organisms in a 
particular environment. Over time, the organisms acquire designs that adapt 
them for survival and reproduction in that environment, period; nothing pulls 
them in any direction other than success there and then.” Pinker concludes that 
“life is a densely branching bush, not a scale or a ladder, and living organisms 
are at the tips of branches, not on lower rungs.” 

With regard to the human brain, he questions whether the “benefits 
outweigh the costs.” Among the costs, he cites that “the brain [is] bulky. The 
female pelvis barely accommodates a baby’s outsized head. That design 
compromise kills many women during childbirth and requires a pivoting gait 
that makes women biomechanically less efficient walkers than men. Also a 
heavy head bobbing around on a neck makes us more vulnerable to fatal injuries 
in accidents such as falls.” He goes on to list additional shortcomings, including 
the brain’s energy consumption, its slow reaction time, and the lengthy process 
of learning. 

While each of these statements is accurate on its face (although many of my 
female friends are better walkers than I am), Pinker is missing the overall point 
here. It is true that biologically, evolution has no specific direction. It is a search 
method that indeed thoroughly fills out the “densely branching bush” of nature. 
It is likewise true that evolutionary changes do not necessarily move in the 
direction of greater intelligence—they move in all directions. There are many 
examples of successful creatures that have remained relatively unchanged for 
millions of years. (Alligators, for instance, date back 200 million years, and 
many microorganisms go back much further than that.) But in the course of 
thoroughly filling out myriad evolutionary branches, one of the directions it does 
move in is toward greater intelligence. That is the relevant point for the purposes 
of this discussion. 

- Neocortex - 

The neocortex covers the entire brain 
with its convoluted thin surface and is 
separated in two halves, connected 
by the corpus callosum. 

Sensorimotor Left Right 

/ a rea hemisphere hemisphere 

„ Visual 

















Physical layout of key regions of the brain. 

The neocortex in different mammals. 

Suppose we have a blue gas in a jar. When we remove the lid, there is no 
message that goes out to all of the molecules of the gas saying, “Hey, guys, the 
lid is off the jar; let’s head up toward the opening and out to freedom.” The 
molecules just keep doing what they always do, which is to move every which 
way with no seeming direction. But in the course of doing so, some of them near 
the top will indeed move out of the jar, and over time most of them will follow 
suit. Once biological evolution stumbled on a neural mechanism capable of 
hierarchical learning, it found it to be immensely useful for evolution’s one 
objective, which is survival. The benefit of having a neocortex became acute 
when quickly changing circumstances favored rapid learning. Species of all 
kinds—plants and animals—can learn to adapt to changing circumstances over 
time, but without a neocortex they must use the process of genetic evolution. It 
can take a great many generations—thousands of years—for a species without a 
neocortex to learn significant new behaviors (or in the case of plants, other 
adaptation strategies). The salient survival advantage of the neocortex was that it 
could learn in a matter of days. If a species encounters dramatically changed 
circumstances and one member of that species invents or discovers or just 
stumbles upon (these three methods all being variations of innovation) a way to 
adapt to that change, other individuals will notice, learn, and copy that method, 
and it will quickly spread virally to the entire population. The cataclysmic 

Cretaceous-Paleogene extinction event about 65 million years ago led to the 
rapid demise of many non-neocortex-bearing species that could not adapt 
quickly enough to a suddenly altered environment. This marked the turning point 
for neocortex-capable mammals to take over their ecological niche. In this way, 
biological evolution found that the hierarchical learning of the neocortex was so 
valuable that this region of the brain continued to grow in size until it virtually 
took over the brain of Homo sapiens. 

Discoveries in neuroscience have established convincingly the key role 
played by the hierarchical capabilities of the neocortex as well as offered 
evidence for the pattern recognition theory of mind (PRTM). This evidence is 
distributed among many observations and analyses, a portion of which I will 
review here. Canadian psychologist Donald O. Hebb (1904-1985) made an 
initial attempt to explain the neurological basis of learning. In 1949 he described 
a mechanism in which neurons change physiologically based on their 
experience, thereby providing a basis for learning and brain plasticity: “Let us 
assume that the persistence or repetition of a reverberatory activity (or Trace’) 
tends to induce lasting cellular changes that add to its stability.... When an axon 
of cell A is near enough to excite a cell B and repeatedly or persistently takes 
part in firing it, some growth process or metabolic change takes place in one or 
both cells such that A’s efficiency, as one of the cells firing B, is increased. 
This theory has been stated as “cells that fire together wire together” and has 
become known as Hebbian learning. Aspects of Hebb’s theory have been 
confirmed, in that it is clear that brain assemblies can create new connections 
and strengthen them, based on their own activity. We can actually see neurons 
developing such connections in brain scans. Artificial “neural nets” are based on 
Hebb’s model of neuronal learning. 

The central assumption in Hebb’s theory is that the basic unit of learning in 
the neocortex is the neuron. The pattern recognition theory of mind that I 
articulate in this book is based on a different fundamental unit: not the neuron 
itself, but rather an assembly of neurons, which I estimate to number around a 
hundred. The wiring and synaptic strengths within each unit are relatively stable 
and determined genetically—that is, the organization within each pattern 
recognition module is determined by genetic design. Learning takes place in the 
creation of connections between these units, not within them, and probably in the 
synaptic strengths of those interunit connections. 

Recent support for the basic module of learning’s being a module of dozens 
of neurons comes from Swiss neuroscientist Henry Markram (born in 1962), 
whose ambitious Blue Brain Project to simulate the entire human brain I 
describe in chapter 7 . In a 2011 paper he describes how while scanning and 

analyzing actual mammalian neocortex neurons, he was “searching] for 
evidence of Hebbian assemblies at the most elementary level of the cortex.” 
What he found instead, he writes, were “elusive assemblies [whose] connectivity 
and synaptic weights are highly predictable and constrained.” He concludes that 
“these findings imply that experience cannot easily mold the synaptic 
connections of these assemblies” and speculates that “they serve as innate, Lego¬ 
like building blocks of knowledge for perception and that the acquisition of 
memories involves the combination of these building blocks into complex 
constructs.” He continues: 

Functional neuronal assemblies have been reported for decades, but 
direct evidence of clusters of synaptically connected neurons...has been 
missing.... Since these assemblies will all be similar in topology and 
synaptic weights, not molded by any specific experience, we consider these 
to be innate assemblies.... Experience plays only a minor role in 
determining synaptic connections and weights within these assemblies.... 
Our study found evidence [of] innate Lego-like assemblies of a few dozen 
neurons.... Connections between assemblies may combine them into super¬ 
assemblies within a neocortical layer, then in higher-order assemblies in a 
cortical column, even higher-order assemblies in a brain region, and finally 
in the highest possible order assembly represented by the whole brain.... 
Acquiring memories is very similar to building with Lego. Each assembly 
is equivalent to a Lego block holding some piece of elementary innate 
knowledge about how to process, perceive and respond to the world.... 
When different blocks come together, they therefore form a unique 
combination of these innate percepts that represents an individual’s specific 
knowledge and experience. - 

The “Lego blocks” that Markram proposes are fully consistent with the 
pattern recognition modules that I have described. In an e-mail communication, 
Markram described these “Lego blocks” as “shared content and innate 
knowledge.’” I would articulate that the purpose of these modules is to recognize 
patterns, to remember them, and to predict them based on partial patterns. Note 
that Markram’s estimate of each module’s containing “several dozen neurons” is 
based only on layer V of the neocortex. Layer V is indeed neuron rich, but based 
on the usual ratio of neuron counts in the six layers, this would translate to an 
order of magnitude of about 100 neurons per module, which is consistent with 
my estimates. 

The consistent wiring and apparent modularity of the neocortex has been 

noted for many years, but this study is the first to demonstrate the stability of 
these modules as the brain undergoes its dynamic processes. 

Another recent study, this one from Massachusetts General Hospital, 
funded by the National Institutes of Health and the National Science Foundation 
and published in a March 2012 issue of the journal Science, also shows a regular 
structure of connections across the neocortex.- The article describes the wiring 
of the neocortex as following a grid pattern, like orderly city streets: “Basically, 
the overall structure of the brain ends up resembling Manhattan, where you have 
a 2-D plan of streets and a third axis, an elevator going in the third dimension,” 
wrote Van J. Wedeen, a Harvard neuroscientist and physicist and the head of the 

In a Science magazine podcast, Wedeen described the significance of the 
research: “This was an investigation of the three-dimensional structure of the 
pathways of the brain. When scientists have thought about the pathways of the 
brain for the last hundred years or so, the typical image or model that comes to 
mind is that these pathways might resemble a bowl of spaghetti—separate 
pathways that have little particular spatial pattern in relation to one another. 
Using magnetic resonance imaging, we were able to investigate this question 
experimentally. And what we found was that rather than being haphazardly 
arranged or independent pathways, we find that ah of the pathways of the brain 
taken together fit together in a single exceedingly simple structure. They 
basically look like a cube. They basically run in three perpendicular directions, 
and in each one of those three directions the pathways are highly parallel to each 
other and arranged in arrays. So, instead of independent spaghettis, we see that 
the connectivity of the brain is, in a sense, a single coherent structure.” 

Whereas the Markram study shows a module of neurons that repeats itself 
across the neocortex, the Wedeen study demonstrates a remarkably orderly 
pattern of connections between modules. The brain starts out with a very large 
number of “connections-in-waiting” to which the pattern recognition modules 
can hook up. Thus if a given module wishes to connect to another, it does not 
need to grow an axon from one and a dendrite from the other to span the entire 
physical distance between them. It can simply harness one of these axonal 
connections-in-waiting and just hook up to the ends of the fiber. As Wedeen and 
his colleagues write, “The pathways of the brain follow a base-plan established 
by...early embryogenesis. Thus, the pathways of the mature brain present an 
image of these three primordial gradients, physically deformed by 
development.” In other words, as we learn and have experiences, the pattern 
recognition modules of the neocortex are connecting to these preestablished 
connections that were created when we were embryos. 

There is a type of electronic chip called a field programmable gate array 
(FPGA) that is based on a similar principle. The chip contains millions of 
modules that implement logic functions along with connections-in-waiting. At 
the time of use, these connections are either activated or deactivated (through 
electronic signals) to implement a particular capability. 

In the neocortex, those long-distance connections that are not used are 
eventually pruned away, which is one reason why adapting a nearby region of 
the neocortex to compensate for one that has become damaged is not quite as 
effective as using the original region. According to the Wedeen study, the initial 
connections are extremely orderly and repetitive, just like the modules 
themselves, and their grid pattern is used to “guide connectivity” in the 
neocortex. This pattern was found in all of the primate and human brains studied 
and was evident across the neocortex, from regions that dealt with early sensory 
patterns up to higher-level emotions. Wedeen’s Science journal article concluded 
that the “grid structure of cerebral pathways was pervasive, coherent, and 
continuous with the three principal axes of development.” This again speaks to a 
common algorithm across all neocortical functions. 

It has long been known that at least certain regions of the neocortex are 
hierarchical. The best-studied region is the visual cortex, which is separated into 
areas known as VI, V2, and MT (also known as V5). As we advance to higher 
areas in this region (“higher” in the sense of conceptual processing, not 
physically, as the neocortex is always just one pattern recognizer thick), the 
properties that can be recognized become more abstract. VI recognizes very 
basic edges and primitive shapes. V2 can recognize contours, the disparity of 
images presented by each of the eyes, spatial orientation, and whether or not a 
portion of the image is part of an object or the background.- Higher-level regions 
of the neocortex recognize concepts such as the identity of objects and faces and 
their movement. It has also long been known that communication through this 
hierarchy is both upward and downward, and that signals can be both excitatory 
and inhibitory. MIT neuroscientist Tomaso Poggio (born in 1947) has 
extensively studied vision in the human brain, and his research for the last thirty- 
five years has been instrumental in establishing hierarchical learning and pattern 
recognition in the “early” (lowest conceptual) levels of the visual neocortex. 

The highly regular grid structure of initial connections in the neocortex 
found in a National Institutes of Health study. 

Another view of the regular grid structure of neocortical connections. 

The grid structure found in the neocortex is remarkably similar to what 
is called crossbar switching, which is used in integrated circuits and circuit 

Our understanding of the lower hierarchical levels of the visual neocortex is 
consistent with the PRTM I described in the previous chapter , and observation of 
the hierarchical nature of neocortical processing has recently extended far 
beyond these levels. University of Texas neurobiology professor Daniel J. 
Felleman and his colleagues traced the “hierarchical organization of the cerebral 
cortex...[in] 25 neocortical areas,” which included both visual areas and higher- 
level areas that combine patterns from multiple senses. What they found as they 
went up the neocortical hierarchy was that the processing of patterns became 
more abstract, comprised larger spatial areas, and involved longer time periods. 
With every connection they found communication both up and down the 

Recent research allows us to substantially broaden these observations to 
regions well beyond the visual cortex and even to the association areas, which 
combine inputs from multiple senses. A study published in 2008 by Princeton 
psychology professor Uri Hasson and his colleagues demonstrates that the 
phenomena observed in the visual cortex occur across a wide variety of 
neocortical areas: “It is well established that neurons along the visual cortical 
pathways have increasingly larger spatial receptive fields. This is a basic 
organizing principle of the visual system.... Real-world events occur not only 
over extended regions of space, but also over extended periods of time. We 
therefore hypothesized that a hierarchy analogous to that found for spatial 
receptive field sizes should also exist for the temporal response characteristics of 

different brain regions.” This is exactly what they found, which enabled them to 
conclude that “similar to the known cortical hierarchy of spatial receptive fields, 
there is a hierarchy of progressively longer temporal receptive windows in the 
human brain.”- 

The most powerful argument for the universality of processing in the 
neocortex is the pervasive evidence of plasticity (not just learning but 
interchangeability): In other words, one region is able to do the work of other 
regions, implying a common algorithm across the entire neocortex. A great deal 
of neuroscience research has been focused on identifying which regions of the 
neocortex are responsible for which types of patterns. The classical technique for 
determining this has been to take advantage of brain damage from injury or 
stroke and to correlate lost functionality with specific damaged regions. So, for 
example, when we notice that someone with newly acquired damage to the 
fusiform gyrus region suddenly has difficulty recognizing faces but is still able 
to identify people from their voices and language patterns, we can hypothesize 
that this region has something to do with face recognition. The underlying 
assumption has been that each of these regions is designed to recognize and 
process a particular type of pattern. Particular physical regions have become 
associated with particular types of patterns, because under normal circumstances 
that is how the information happens to flow. But when that normal flow of 
information is disrupted for any reason, another region of the neocortex is able to 
step in and take over. 

Plasticity has been widely noted by neurologists, who observed that patients 
with brain damage from an injury or a stroke can relearn the same skills in 
another area of the neocortex. Perhaps the most dramatic example of plasticity is 
a 2011 study by American neuroscientist Marina Bedny and her colleagues on 
what happens to the visual cortex of congenitally blind people. The common 
wisdom has been that the early layers of the visual cortex, such as VI and V2, 
inherently deal with very low-level patterns (such as edges and curves), whereas 
the frontal cortex (that evolutionarily new region of the cortex that we have in 
our uniquely large foreheads) inherently deals with the far more complex and 
subtle patterns of language and other abstract concepts. But as Bedny and her 
colleagues found, “Humans are thought to have evolved brain regions in the left 
frontal and temporal cortex that are uniquely capable of language processing. 
However, congenitally blind individuals also activate the visual cortex in some 
verbal tasks. We provide evidence that this visual cortex activity in fact reflects 
language processing. We find that in congenitally blind individuals, the left 
visual cortex behaves similarly to classic language regions.... We conclude that 
brain regions that are thought to have evolved for vision can take on language 

processing as a result of early experience.”— 

Consider the implications of this study: It means that neocortical regions 
that are physically relatively far apart, and that have also been considered 
conceptually very different (primitive visual cues versus abstract language 
concepts), use essentially the same algorithm. The regions that process these 
disparate types of patterns can substitute for one another. 

University of California at Berkeley neuroscientist Daniel E. Feldman 
wrote a comprehensive 2009 review of what he called “synaptic mechanisms for 
plasticity in the neocortex” and found evidence for this type of plasticity across 
the neocortex. He writes that “plasticity allows the brain to learn and remember 
patterns in the sensory world, to refine movements...and to recover function 
after injury.” He adds that this plasticity is enabled by “structural changes 
including formation, removal, and morphological remodeling of cortical 
synapses and dendritic spines.”— 

Another startling example of neocortical plasticity (and therefore of the 
uniformity of the neocortical algorithm) was recently demonstrated by scientists 
at the University of California at Berkeley. They hooked up implanted 
microelectrode arrays to pick up brain signals specifically from a region of the 
motor cortex of mice that controls the movement of their whiskers. They set up 
their experiment so that the mice would get a reward if they controlled these 
neurons to fire in a certain mental pattern but not to actually move their 
whiskers. The pattern required to get the reward involved a mental task that their 
frontal neurons would normally not do. The mice were nonetheless able to 
perform this mental feat essentially by thinking with their motor neurons while 
mentally decoupling them from controlling motor movements.— The conclusion 
is that the motor cortex, the region of the neocortex responsible for coordinating 
muscle movement, also uses the standard neocortical algorithm. 

There are several reasons, however, why a skill or an area of knowledge 
that has been relearned using a new area of the neocortex to replace one that has 
been damaged will not necessarily be as good as the original. First, because it 
took an entire lifetime to learn and perfect a given skill, relearning it in another 
area of the neocortex will not immediately generate the same results. More 
important, that new area of the neocortex has not just been sitting around waiting 
as a standby for an injured region. It too has been carrying out vital functions, 
and will therefore be hesitant to give up its neocortical patterns to compensate 
for the damaged region. It can start by releasing some of the redundant copies of 
its patterns, but doing so will subtly degrade its existing skills and does not free 
up as much cortical space as the skills being relearned had used originally. 

There is a third reason why plasticity has its limits. Since in most people 
particular types of patterns will flow through specific regions (such as faces 
being processed by the fusiform gyrus), these regions have become optimized 
(by biological evolution) for those types of patterns. As I report in chapter 7 . we 
found the same result in our digital neocortical developments. We could 
recognize speech with our character recognition systems and vice versa, but the 
speech systems were optimized for speech and similarly the character 
recognition systems were optimized for printed characters, so there would be 
some reduction in performance if we substituted one for the other. We actually 
used evolutionary (genetic) algorithms to accomplish this optimization, a 
simulation of what biology does naturally. Given that faces have been flowing 
through the fusiform gyrus for most people for hundreds of thousands of years 
(or more), biological evolution has had time to evolve a favorable ability to 
process such patterns in that region. It uses the same basic algorithm, but it is 
oriented toward faces. As Dutch neuroscientist Randal Koene wrote, “The 
[neo]cortex is very uniform, each column or minicolumn can in principle do 
what each other one can do.”— 

Substantial recent research supports the observation that the pattern 
recognition modules wire themselves based on the patterns to which they are 
exposed. For example, neuroscientist Yi Zuo and her colleagues watched as new 
“dendritic spines” formed connections between nerve cells as mice learned a 
new skill (reaching through a slot to grab a seed).— Researchers at the Salk 
Institute have discovered that this critical self-wiring of the neocortex modules is 
apparently controlled by only a handful of genes. These genes and this method 
of self-wiring are also uniform across the neocortex.— 

Many other studies document these attributes of the neocortex, but let’s 
summarize what we can observe from the neuroscience literature and from our 
own thought experiments. The basic unit of the neocortex is a module of 
neurons, which I estimate at around a hundred. These are woven together into 
each neocortical column so that each module is not visibly distinct. The pattern 
of connections and synaptic strengths within each module is relatively stable. It 
is the connections and synaptic strengths between modules that represent 

There are on the order of a quadrillion (10 15 ) connections in the neocortex, 
yet only about 25 million bytes of design information in the genome (after 
lossless compression),— so the connections themselves cannot possibly be 
predetermined genetically. It is possible that some of this learning is the product 
of the neocortex’s interrogating the old brain, but that still would necessarily 

represent only a relatively small amount of information. The connections 
between modules are created on the whole from experience (nurture rather than 

The brain does not have sufficient flexibility so that each neocortical pattern 
recognition module can simply link to any other module (as we can easily 
program in our computers or on the Web)—an actual physical connection must 
be made, composed of an axon connecting to a dendrite. We each start out with a 
vast stockpile of possible neural connections. As the Wedeen study shows, these 
connections are organized in a very repetitive and orderly manner. Terminal 
connection to these axons-in-waiting takes place based on the patterns that each 
neocortical pattern recognizer has recognized. Unused connections are 
ultimately pruned away. These connections are built hierarchically, reflecting the 
natural hierarchical order of reality. That is the key strength of the neocortex. 

The basic algorithm of the neocortical pattern recognition modules is 
equivalent across the neocortex from “low-level” modules, which deal with the 
most basic sensory patterns, to “high-level” modules, which recognize the most 
abstract concepts. The vast evidence of plasticity and the interchangeability of 
neocortical regions is testament to this important observation. There is some 
optimization of regions that deal with particular types of patterns, but this is a 
second-order effect—the fundamental algorithm is universal. 

Signals go up and down the conceptual hierarchy. A signal going up means, 
“I’ve detected a pattern.” A signal going down means, “I’m expecting your 
pattern to occur,” and is essentially a prediction. Both upward and downward 
signals can be either excitatory or inhibitory. 

Each pattern is itself in a particular order and is not readily reversed. Even 
if a pattern appears to have multidimensional aspects, it is represented by a one¬ 
dimensional sequence of lower-level patterns. A pattern is an ordered sequence 
of other patterns, so each recognizer is inherently recursive. There can be many 
levels of hierarchy. 

There is a great deal of redundancy in the patterns we learn, especially the 
important ones. The recognition of patterns (such as common objects and faces) 
uses the same mechanism as our memories, which are just patterns we have 
learned. They are also stored as sequences of patterns—they are basically 
stories. That mechanism is also used for learning and carrying out physical 
movement in the world. The redundancy of patterns is what enables us to 
recognize objects, people, and ideas even when they have variations and occur in 
different contexts. The size and size variability parameters also allow the 
neocortex to encode variation in magnitude against different dimensions 
(duration in the case of sound). One way that these magnitude parameters could 

be encoded is simply through multiple patterns with different numbers of 
repeated inputs. So, for example, there could be patterns for the spoken word 
“steep” with different numbers of the long vowel [E] repeated, each with the 
importance parameter set to a moderate level indicating that the repetition of [E] 
is variable. This approach is not mathematically equivalent to having the explicit 
size parameters and does not work nearly as well in practice, but is one approach 
to encoding magnitude. The strongest evidence we have for these parameters is 
that they are needed in our AI systems to get accuracy levels that are near human 

The summary above constitutes the conclusions we can draw from the 
sampling of research results I have shared above as well as the sampling of 
thought experiments I discussed earlier. I maintain that the model I have 
presented is the only possible model that satisfies all of the constraints that the 
research and our thought experiments have established. 

Finally, there is one more piece of corroborating evidence. The techniques 
that we have evolved over the past several decades in the field of artificial 
intelligence to recognize and intelligently process real-world phenomena (such 
as human speech and written language) and to understand natural-language 
documents turn out to be mathematically similar to the model I have presented 
above. They are also examples of the PRTM. The AI field was not explicitly 
trying to copy the brain, but it nonetheless arrived at essentially equivalent 



I have an old brain but a terrific memory. 

—A1 Lewis 

Here we stand in the middle of this new world with our primitive brain, 
attuned to the simple cave life, with terrific forces at our disposal, which we 
are clever enough to release, but whose consequences we cannot 

—Albert Szent-Gyorgyi 

Our old brain—the one we had before we were mammals—has not 
disappeared. Indeed it still provides much of our motivation in seeking 
gratification and avoiding danger. These goals are modulated, however, by our 
neocortex, which dominates the human brain in both mass and activity. 

Animals used to live and survive without a neocortex, and indeed all 
nonmammalian animals continue to do so today. We can view the human 
neocortex as the great sublimator—thus our primitive motivation to avoid a large 
predator may be transformed by the neocortex today into completing an 
assignment to impress our boss; the great hunt may become writing a book on, 
say, the mind; and pursuing reproduction may become gaining public 
recognition or decorating your apartment. (Well, this last motivation is not 
always so hidden.) 

The neocortex is likewise good at helping us solve problems because it can 
accurately model the world, reflecting its true hierarchical nature. But it is the 
old brain that presents us with those problems. Of course, like any clever 
bureaucracy, the neocortex often deals with the problems it is assigned by 
redefining them. On that note, let’s review the information processing in the old 

The Sensory Pathway 

Pictures, propagated by motion along the fibers of the optic nerves in the 
brain, are the cause of vision. 

—Isaac Newton 

Each of us lives within the universe—the prison—of his own brain. 
Projecting from it are millions of fragile sensory nerve fibers, in groups 
uniquely adapted to sample the energetic states of the world around us: 
heat, light, force, and chemical composition. That is all we ever know of it 
directly; all else is logical inference. 

—Vernon Mountcastle 1 

Although we experience the illusion of receiving high-resolution images from 
our eyes, what the optic nerve actually sends to the brain is just a series of 
outlines and clues about points of interest in our visual field. We then essentially 
hallucinate the world from cortical memories that interpret a series of movies 
with very low data rates that arrive in parallel channels. In a study published in 
Nature, Frank S. Werblin, professor of molecular and cell biology at the 
University of California at Berkeley, and doctoral student Boton Roska, MD, 
showed that the optic nerve carries ten to twelve output channels, each of which 
carries only a small amount of information about a given scene.- One group of 
what are called ganglion cells sends information only about edges (changes in 
contrast). Another group detects only large areas of uniform color, whereas a 
third group is sensitive only to the backgrounds behind figures of interest. 

The visual pathway in the brain. 

“Even though we think we see the world so fully, what we are receiving is 
really just hints, edges in space and time,” says Werblin. “These 12 pictures of 
the world constitute all the information we will ever have about what’s out there, 
and from these 12 pictures, which are so sparse, we reconstruct the richness of 
the visual world. I’m curious how nature selected these 12 simple movies and 
how it can be that they are sufficient to provide us with all the information we 
seem to need.” 

This data reduction is what in the AI field we call “sparse coding.” We have 
found in creating artificial systems that throwing most of the input information 
away and retaining only the most salient details provides superior results. 
Otherwise the limited ability to process information in a neocortex (biological or 
otherwise) gets overwhelmed. 

Seven of the twelve low-data-rate “movies” sent by the optic nerve to 
the brain. 

The processing of auditory information from the human cochlea through the 
subcortical regions and then through the early stages of the neocortex has been 
meticulously modeled by Lloyd Watts and his research team at Audience, Inc.- 
They have developed research technology that extracts 600 different frequency 
bands (60 per octave) from sound. This comes much closer to the estimate of 
3,000 bands extracted by the human cochlea (compared with commercial speech 
recognition, which uses only 16 to 32 bands). Using two microphones and its 
detailed (and high-spectral resolution) model of auditory processing, Audience 
has created a commercial technology (with somewhat lower spectral resolution 
than its research system) that effectively removes background noise from 
conversations. This is now being used in many popular cell phones and is an 
impressive example of a commercial product based on an understanding of how 
the human auditory perceptual system is able to focus on one sound source of 

The auditory pathway in the brain. 

Inputs from the body (estimated at hundreds of megabits per second), 
including that of nerves from the skin, muscles, organs, and other areas, stream 
into the upper spinal cord. These messages involve more than just 
communication about touch; in addition they carry information about 
temperature, acid levels (for example, lactic acid in muscles), the movement of 
food through the gastrointestinal tract, and many other signals. This data is 
processed through the brain stem and midbrain. Key cells called lamina 1 
neurons create a map of the body, representing its current state, not unlike the 
displays used by flight controllers to track airplanes. From here the sensory data 
heads to a mysterious region called the thalamus, which brings us to our next 

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A simplified model of auditory processing in both the subcortical areas 
(areas prior to the neocortex) and the neocortex, created by Audience, Inc. 
Figure adapted from L. Watts, “Reverse-Engineering the Human Auditory 
Pathway,” in J. Liu et al. (eds.), WCCI 2012 (Berlin: Springer-Verlag, 
2012), p. 49. 

The Thalamus 

Everyone knows what attention is. It is the taking possession by the mind, 
in clear and vivid form, of one out of what seem several simultaneously 
possible objects or trains of thought. Focalization, concentration, of 
consciousness, are of its essence. It implies withdrawal from some things in 
order to deal effectively with others. 

—William James 

From the midbrain, sensory information then flows through a nut-sized region 
called the posterior ventromedial nucleus (VMpo) of the thalamus, which 
computes complex reactions to bodily states such as “this tastes terrible,” “what 
a stench,” or “that light touch is stimulating.” The increasingly processed 
information ends up at two regions of the neocortex called the insula. These 
structures, the size of small fingers, are located on the left and right sides of the 
neocortex. Dr. Arthur Craig of the Barrow Neurological Institute in Phoenix 
describes the VMpo and the two insula regions as “a system that represents the 
material me.”- 

Among its other functions, the thalamus is considered a gateway for 
preprocessed sensory information to enter the neocortex. In addition to the tactile 
information flowing through the VMpo, processed information from the optic 
nerve (which, as noted above, has already been substantially transformed) is sent 
to a region of the thalamus called the lateral geniculate nucleus, which then 
sends it on to the VI region of the neocortex. Information from the auditory 
sense is passed through the medial geniculate nucleus of the thalamus en route to 
the early auditory regions of the neocortex. All of our sensory data (except, 
apparently, for the olfactory system, which uses the olfactory bulb instead) 
passes through specific regions of the thalamus. 

The most significant role of the thalamus, however, is its continual 
communication with the neocortex. The pattern recognizers in the neocortex 
send tentative results to the thalamus and receive responses principally using 
both excitatory and inhibitory reciprocal signals from layer VI of each 
recognizer. Keep in mind that these are not wireless messages, so that there 
needs to be an extraordinary amount of actual wiring (in the form of axons) 
mnning between all regions of the neocortex and the thalamus. Consider the vast 
amount of real estate (in terms of the physical mass of connections required) for 
the hundreds of millions of pattern recognizers in the neocortex to be constantly 

checking in with the thalamus.- 

So what are the hundreds of millions of neocortical pattern recognizers 
talking to the thalamus about? It is apparently an important conversation, 
because profound damage to the main region of the thalamus bilaterally can lead 
to prolonged unconsciousness. A person with a damaged thalamus may still have 
activity in his neocortex, in that the self-triggering thinking by association can 
still work. But directed thinking—the kind that will get us out of bed, into our 
car, and sitting at our desk at work—does not function without a thalamus. In a 
famous case, twenty-one-year-old Karen Ann Quinlan suffered a heart attack and 
respiratory failure and remained in an unresponsive, apparently vegetative state 
for ten years. When she died, her autopsy revealed that her neocortex was 
normal but her thalamus had been destroyed. 

In order to play its key role in our ability to direct attention, the thalamus 
relies on the structured knowledge contained in the neocortex. It can step 
through a list (stored in the neocortex), enabling us to follow a train of thought 
or follow a plan of action. We are apparently able to keep up to about four items 
in our working memory at a time, two per hemisphere according to recent 
research by neuroscientists at the MIT Picower Institute for Learning and 
Memory.- The issue of whether the thalamus is in charge of the neocortex or 
vice versa is far from clear, but we are unable to function without both. 

The Hippocampus 

Each brain hemisphere contains a hippocampus, a small region that looks like a 
sea horse tucked in the medial temporal lobe. Its primary function is to 
remember novel events. Since sensory information flows through the neocortex, 
it is up to the neocortex to determine that an experience is novel in order to 
present it to the hippocampus. It does so either by failing to recognize a 
particular set of features (for example, a new face) or by realizing that an 
otherwise familiar situation now has unique attributes (such as your spouse’s 
wearing a fake mustache). 

The hippocampus is capable of remembering these situations, although it 
appears to do so primarily through pointers into the neocortex. So memories in 
the hippocampus are also stored as lower-level patterns that were earlier 
recognized and stored in the neocortex. For animals without a neocortex to 
modulate sensory experiences, the hippocampus will simply remember the 
information from the senses, although this will have undergone sensory 
preprocessing (for example, the transformations performed by the optic nerve). 

Although the hippocampus makes use of the neocortex (if a particular brain 
has one) as its scratch pad, its memory (of pointers into the neocortex) is not 
inherently hierarchical. Animals without a neocortex can accordingly remember 
things using their hippocampus, but their recollections will not be hierarchical. 

The capacity of the hippocampus is limited, so its memory is short-term. It 
will transfer a particular sequence of patterns from its short-term memory to the 
long-term hierarchical memory of the neocortex by playing this memory 
sequence to the neocortex over and over again. We need, therefore, a 
hippocampus in order to learn new memories and skills (although strictly motor 
skills appear to use a different mechanism). Someone with damage to both 
copies of her hippocampus will retain her existing memories but will not be able 
to form new ones. 

University of Southern California neuroscientist Theodore Berger and his 
colleagues modeled the hippocampus of a rat and have successfully 
experimented with implanting an artificial one. In a study reported in 2011, the 
USC scientists blocked particular learned behaviors in rats with drugs. Using an 
artificial hippocampus, the rats were able to quickly relearn the behavior. “Flip 
the switch on, and the rats remember. Flip it off and the rats forget,” Berger 
wrote, referring to his ability to control the neural implant remotely. In another 

experiment the scientists allowed their artificial hippocampus to work alongside 
the rats’ natural one. The result was that the ability of the rats to learn new 
behaviors strengthened. “These integrated experimental modeling studies show 
for the first time,” Berger explained, “that...a neural prosthesis capable of real¬ 
time identification and manipulation of the encoding process can restore and 
even enhance cognitive mnemonic processes.”- The hippocampus is one of the 
first regions damaged by Alzheimer’s, so one goal of this research is to develop 
a neural implant for humans that will mitigate this first phase of damage from 
the disease. 

The Cerebellum 

There are two approaches you can use to catch a fly ball. You could solve the 
complex simultaneous differential equations controlling the ball’s movement as 
well as further equations governing your own particular angle in viewing the 
ball, and then compute even more equations on how to move your body, arm, 
and hand to be in the right place at the right time. 

This is not the approach that your brain adopts. It basically simplifies the 
problem by collapsing a lot of equations into a simple trend model, considering 
the trends of where the ball appears to be in your field of vision and how quickly 
it is moving within it. It does the same thing with your hand, making essentially 
linear predictions of the ball’s apparent position in your field of view and that of 
your hand. The goal, of course, is to make sure they meet at the same point in 
space and time. If the ball appears to be dropping too quickly and your hand 
appears to be moving too slowly, your brain will direct your hand to move more 
quickly, so that the trends will coincide. This “Gordian knot” solution to what 
would otherwise be an intractable mathematical problem is called basis 
functions, and they are carried out by the cerebellum, a bean-shaped and 
appropriately baseball-sized region that sits on the brain stem.- 

The cerebellum is an old-brain region that once controlled virtually all 
hominid movements. It still contains half of the neurons in the brain, although 
most are relatively small ones, so the region constitutes only about 10 percent of 
the weight of the brain. The cerebellum likewise represents another instance of 
massive repetition in the design of the brain. There is relatively little information 
about its design in the genome, as its structure is a pattern of several neurons that 
is repeated billions of times. As with the neocortex, there is uniformity across its 
structure. 2 

Most of the function of controlling our muscles has been taken over by the 
neocortex, using the same pattern recognition algorithms that it uses for 
perception and cognition. In the case of movement, we can more appropriately 
refer to the neocortex’s function as pattern implementation. The neocortex does 
make use of the memory in the cerebellum to record delicate scripts of 
movements—for example, your signature and certain flourishes in artistic 
expression such as music and dance. Studies of the role of the cerebellum during 
the learning of handwriting by children reveal that the Purkinje cells of the 
cerebellum actually sample the sequence of movements, with each one sensitive 

to a specific sample.— Because most of our movement is now controlled by the 
neocortex, many people can manage with a relatively modest obvious disability 
even with significant damage to the cerebellum, except that their movements 
may become less graceful. 

The neocortex can also call upon the cerebellum to use its ability to 
compute real-time basis functions to anticipate what the results of actions would 
be that we are considering but have not yet carried out (and may never carry 
out), as well as the actions or possible actions of others. It is another example of 
the innate built-in linear predictors in the brain. 

Substantial progress has been made in simulating the cerebellum with 
respect to the ability to respond dynamically to sensory cues using the basis 
functions I discussed above, in both bottom-up simulations (based on 
biochemical models) and top-down simulations (based on mathematical models 
of how each repeating unit in the cerebellum operates).— 

Pleasure and Fear 

Fear is the main source of superstition, and one of the main sources of 
cruelty. To conquer fear is the beginning of wisdom. 

—Bertrand Russell 

Feel the fear and do it anyway. 

—Susan Jeffers 

If the neocortex is good at solving problems, then what is the main problem we 
are trying to solve? The problem that evolution has always tried to solve is 
survival of the species. That translates into the survival of the individual, and 
each of us uses his or her own neocortex to interpret that in myriad ways. In 
order to survive, animals need to procure their next meal while at the same time 
avoiding becoming someone else’s meal. They also need to reproduce. The 
earliest brains evolved pleasure and fear systems that rewarded the fulfillment of 
these fundamental needs along with basic behaviors that facilitated them. As 
environments and competing species gradually changed, biological evolution 
made corresponding alterations. With the advent of hierarchical thinking, the 
satisfaction of critical drives became more complex, as it was now subject to the 
vast complex of ideas within ideas. But despite its considerable modulation by 
the neocortex, the old brain is still alive and well and still motivating us with 
pleasure and fear. 

One region that is associated with pleasure is the nucleus accumbens. In 
famous experiments conducted in the 1950s, rats that were able to directly 
stimulate this small region (by pushing a lever that activated implanted 
electrodes) preferred doing so to anything else, including having sex or eating, 
ultimately exhausting and starving themselves to death.— In humans, other 
regions are also involved in pleasure, such as the ventral pallidum and, of course, 
the neocortex itself. 

Pleasure is also regulated by chemicals such as dopamine and serotonin. It 
is beyond the scope of this book to discuss these systems in detail, but it is 
important to recognize that we have inherited these mechanisms from our 
premammalian cousins. It is the job of our neocortex to enable us to be the 

master of pleasure and fear and not their slave. To the extent that we are often 
subject to addictive behaviors, the neocortex is not always successful in this 
endeavor. Dopamine in particular is a neurotransmitter involved in the 
experience of pleasure. If anything good happens to us—winning the lottery, 
gaining the recognition of our peers, getting a hug from a loved one, or even 
subtle achievements such as getting a friend to laugh at a joke—we experience a 
release of dopamine. Sometimes we, like the rats who died overstimulating their 
nucleus accumbens, use a shortcut to achieve these bursts of pleasure, which is 
not always a good idea. 

Gambling, for example, can release dopamine, at least when you win, but 
this is dependent on its inherent lack of predictability. Gambling may work for 
the purpose of releasing dopamine for a while, but given that the odds are 
intentionally stacked against you (otherwise the business model of a casino 
wouldn’t work), it can become ruinous as a regular strategy. Similar dangers are 
associated with any addictive behavior. A particular genetic mutation of the 
dopamine-receptor D2 gene causes especially strong feelings of pleasure from 
initial experiences with addictive substances and behaviors, but as is well known 
(but not always well heeded), the ability of these substances to produce pleasure 
on subsequent use gradually declines. Another genetic mutation results in 
people’s not receiving normal levels of dopamine release from everyday 
accomplishments, which can also lead to seeking enhanced early experiences 
with addictive activities. The minority of the population that has these genetic 
proclivities to addiction creates an enormous social and medical problem. Even 
those who manage to avoid severely addictive behaviors struggle with balancing 
the rewards of dopamine release with the consequences of the behaviors that 
release them. 

Serotonin is a neurotransmitter that plays a major role in the regulation of 
mood. In higher levels it is associated with feelings of well-being and 
contentment. Serotonin has other functions, including modulating synaptic 
strength, appetite, sleep, sexual desire, and digestion. Antidepression drugs such 
as selective serotonin reuptake inhibitors (which tend to increase serotonin levels 
available to receptors) tend to have far-reaching effects, not all of them desirable 
(such as suppressing libido). Unlike actions in the neocortex, where recognition 
of patterns and activations of axons affect only a small number of neocortical 
circuits at a time, these substances affect large regions of the brain or even the 
entire nervous system. 

Each hemisphere of the human brain has an amygdala, which consists of an 
almond-shaped region comprising several small lobes. The amygdala is also part 
of the old brain and is involved in processing a number of types of emotional 

responses, the most notable of which is fear. In premammalian animals, certain 
preprogrammed stimuli representing danger feed directly into the amygdala, 
which in turn triggers the “fight or flight” mechanism. In humans the amygdala 
now depends on perceptions of danger to be transmitted by the neocortex. A 
negative comment by your boss, for example, might trigger such a response by 
generating the fear of losing your job (or maybe not, if you have confidence in a 
plan B). Once the amygdala does decide that danger is ahead, an ancient 
sequence of events occurs. The amygdala signals the pituitary gland to release a 
hormone called ACTH (adrenocorticotropin). This in turn triggers the stress 
hormone cortisol from the adrenal glands, which results in more energy being 
provided to your muscles and nervous system. The adrenal glands also produce 
adrenaline and noradrenaline, which suppress your digestive, immune, and 
reproductive systems (figuring that these are not high-priority processes in an 
emergency). Levels of blood pressure, blood sugar, cholesterol, and fibrinogen 
(which speeds blood clotting) all rise. Heart rate and respiration go up. Even 
your pupils dilate so that you have better visual acuity of your enemy or your 
escape route. This is all very useful if a real danger such as a predator suddenly 
crosses your path. It is well known that in today’s world, the chronic activation 
of this fight-or-flight mechanism can lead to permanent health damage in terms 
of hypertension, high cholesterol levels, and other problems. 

The system of global neurotransmitter levels, such as serotonin, and 
hormone levels, such as dopamine, is intricate, and we could spend the rest of 
this book on the issue (as a great many books have done), but it is worth pointing 
out that the bandwidth of information (the rate of information processing) in this 
system is very low compared with the bandwidth of the neocortex. There are 
only a limited number of substances involved and the levels of these chemicals 
tend to change slowly and are relatively universal across the brain, as compared 
with the neocortex, which is composed of hundreds of trillions of connections 
that can change quickly. 

It is fair to say that our emotional experiences take place in both the old and 
the new brains. Thinking takes place in the new brain (the neocortex), but 
feeling takes place in both. Any emulation of human behavior will therefore 
need to model both. However, if it is just human cognitive intelligence that we 
are after, the neocortex is sufficient. We can replace the old brain with the more 
direct motivation of a nonbiological neocortex to achieve the goals that we 
assign to it. For example, in the case of Watson, the goal was simply stated: 
Come up with correct answers to Jeopardy! queries (albeit these were further 
modulated by a program that understood Jeopardy! wagering). In the case of the 
new Watson system being jointly developed by Nuance and IBM for medical 

knowledge, the goal is to help treat human disease. Future systems can have 
goals such as actually curing disease and alleviating poverty. A lot of the 
pleasure-fear struggle is already obsolete for humans, as the old brain evolved 
long before even primitive human society got started; indeed most of it is 

There is a continual struggle in the human brain as to whether the old or the 
new brain is in charge. The old brain tries to set the agenda with its control of 
pleasure and fear experiences, whereas the new brain is continually trying to 
understand the relatively primitive algorithms of the old brain and seeking to 
manipulate it to its own agenda. Keep in mind that the amygdala is unable to 
evaluate danger on its own—in the human brain it relies on the neocortex to 
make those judgments. Is that person a friend or a foe, a lover or a threat? Only 
the neocortex can decide. 

To the extent that we are not directly engaged in mortal combat and hunting 
for food, we have succeeded in at least partially sublimating our ancient drives to 
more creative endeavors. On that note, we’ll discuss creativity and love in the 
next chapter . 



This is my simple religion. There is no need for temples; no need for 
complicated philosophy. Our own brain, our own heart is our temple; the 
philosophy is kindness. 

—The Dalai Lama 

My hand moves because certain forces—electric, magnetic, or whatever 
“nerve-force” may prove to be—are impressed on it by my brain. This 
nerve-force, stored in the brain, would probably be traceable, if Science 
were complete, to chemical forces supplied to the brain by the blood, and 
ultimately derived from the food I eat and the air I breathe. 

—Lewis Carroll 

Our emotional thoughts also take place in the neocortex but are influenced by 
portions of the brain ranging from ancient brain regions such as the amygdala to 
some evolutionarily recent brain structures such as the spindle neurons, which 
appear to play a key role in higher-level emotions. Unlike the regular and logical 
recursive structures found in the cerebral cortex, the spindle neurons have highly 
irregular shapes and connections. They are the largest neurons in the human 
brain, spanning its entire breadth. They are deeply interconnected, with hundreds 
of thousands of connections tying together diverse portions of the neocortex. 

As mentioned earlier, the insula helps process sensory signals, but it also 
plays a key role in higher-level emotions. It is this region from which the spindle 
cells originate. Functional magnetic resonance imaging (fMRI) scans have 
revealed that these cells are particularly active when a person is dealing with 
emotions such as love, anger, sadness, and sexual desire. Situations that strongly 
activate them include when a subject looks at her partner or hears her child 

Spindle cells have long neural filaments called apical dendrites, which are 

able to connect to faraway neocortical regions. Such “deep” interconnectedness, 
in which certain neurons provide connections across numerous regions, is a 
feature that occurs increasingly as we go up the evolutionary ladder. It is not 
surprising that the spindle cells, involved as they are in handling emotion and 
moral judgment, would have this form of connectedness, given the ability of 
higher-level emotional reactions to touch on diverse topics and thoughts. 
Because of their links to many other parts of the brain, the high-level emotions 
that spindle cells process are affected by all of our perceptual and cognitive 
regions. It is important to point out that these cells are not doing rational 
problem solving, which is why we don’t have rational control over our responses 
to music or over falling in love. The rest of the brain is heavily engaged, 
however, in trying to make sense of our mysterious high-level emotions. 

There are relatively few spindle cells: only about 80,000, with 
approximately 45,000 in the right hemisphere and 35,000 in the left. This 
disparity is at least one reason for the perception that emotional intelligence is 
the province of the right brain, although the disproportion is modest. Gorillas 
have about 16,000 of these cells, bonobos about 2,100, and chimpanzees about 
1,800. Other mammals lack them completely. 

Anthropologists believe that spindle cells made their first appearance 10 to 
15 million years ago in the as yet undiscovered common ancestor to apes and 
hominids (precursors to humans) and rapidly increased in numbers around 
100,000 years ago. Interestingly, spindle cells do not exist in newborn humans 
but begin to appear only at around the age of four months and increase 
significantly in number from ages one to three. Children’s ability to deal with 
moral issues and perceive such higher-level emotions as love develop during this 
same period. 


Wolfgang Amadeus Mozart (1756-1791) wrote a minuet when he was five. At 
age six he performed for the empress Maria Theresa at the imperial court in 
Vienna. He went on to compose six hundred pieces, including forty-one 
symphonies, before his death at age thirty-five, and is widely regarded as the 
greatest composer in the European classical tradition. One might say that he had 
an aptitude for music. 

So what does this mean in the context of the pattern recognition theory of 
mind? Clearly part of what we regard as aptitude is the product of nurture, that is 
to say, the influences of environment and other people. Mozart was born into a 
musical family. His father, Leopold, was a composer and kapellmeister (literally 
musical leader) of the court orchestra of the archbishop of Salzburg. The young 
Mozart was immersed in music, and his father started teaching him the violin 
and clavier (a keyboard instrument) at the age of three. 

However, environmental influences alone do not fully explain Mozart’s 
genius. There is clearly a nature component as well. What form does this take? 
As I wrote in chapter 4 . different regions of the neocortex have become 
optimized (by biological evolution) for certain types of patterns. Even though the 
basic pattern recognition algorithm of the modules is uniform across the 
neocortex, since certain types of patterns tend to flow through particular regions 
(faces through the fusiform gyrus, for example), those regions will become 
better at processing the associated patterns. However, there are numerous 
parameters that govern how the algorithm is actually carried out in each module. 
For example, how close a match is required for a pattern to be recognized? How 
is that threshold modified if a higher-level module sends a signal that its pattern 
is “expected”? How are the size parameters considered? These and other factors 
have been set differently in different regions to be advantageous for particular 
types of patterns. In our work with similar methods in artificial intelligence, we 
have noticed the same phenomenon and have used simulations of evolution to 
optimize these parameters. 

If particular regions can be optimized for different types of patterns, then it 
follows that individual brains will also vary in their ability to learn, recognize, 
and create certain types of patterns. For example, a brain can have an innate 
aptitude for music by being better able to recognize rhythmic patterns, or to 
better understand the geometric arrangements of harmonies. The phenomenon of 

perfect pitch (the ability to recognize and to reproduce a pitch without an 
external reference), which is correlated with musical talent, appears to have a 
genetic basis, although the ability needs to be developed, so it is likely to be a 
combination of nature and nurture. The genetic basis of perfect pitch is likely to 
reside outside the neocortex in the preprocessing of auditory information, 
whereas the learned aspect resides in the neocortex. 

There are other skills that contribute to degrees of competency, whether of 
the routine variety or of the legendary genius. Neocortical abilities—for 
example, the ability of the neocortex to master the signals of fear that the 
amygdala generates (when presented with disapproval)—play a significant role, 
as do attributes such as confidence, organizational skills, and the ability to 
influence others. A very important skill I noted earlier is the courage to pursue 
ideas that go against the grain of orthodoxy. Invariably, people we regard as 
geniuses pursued their own mental experiments in ways that were not initially 
understood or appreciated by their peers. Although Mozart did gain recognition 
in his lifetime, most of the adulation came later. He died a pauper, buried in a 
common grave, and only two other musicians showed up at his funeral. 


Creativity is a drug I cannot live without. 

—Cecil B. DeMille 

The problem is never how to get new, innovative thoughts into your mind, 
but how to get old ones out. Every mind is a building filled with archaic 
furniture. Clean out a corner of your mind and creativity will instantly fill 

—Dee Hock 

Humanity can be quite cold to those whose eyes see the world differently. 

—Eric A. Burns 

Creativity can solve almost any problem. The creative act, the defeat of 
habit by originality, overcomes everything. 

—George Lois 

A key aspect of creativity is the process of finding great metaphors—symbols 
that represent something else. The neocortex is a great metaphor machine, which 
accounts for why we are a uniquely creative species. Every one of the 
approximately 300 million pattern recognizers in our neocortex is recognizing 
and defining a pattern and giving it a name, which in the case of the neocortical 
pattern recognition modules is simply the axon emerging from the pattern 
recognizer that will fire when that pattern is found. That symbol in turn then 
becomes part of another pattern. Each one of these patterns is essentially a 
metaphor. The recognizers can fire up to 100 times a second, so we have the 
potential of recognizing up to 30 billion metaphors a second. Of course not every 
module is firing in every cycle—but it is fair to say that we are indeed 
recognizing millions of metaphors a second. 

Of course, some metaphors are more significant than others. Darwin 
perceived that Charles Lyell’s insight on how very gradual changes from a 

trickle of water could carve out great canyons was a powerful metaphor for how 
a trickle of small evolutionary changes over thousands of generations could 
carve out great changes in the differentiation of species. Thought experiments, 
such as the one that Einstein used to illuminate the true meaning of the 
Michelson-Morley experiment, are all metaphors, in the sense of being a “thing 
regarded as representative or symbolic of something else,” to quote a dictionary 

Do you see any metaphors in Sonnet 73 by Shakespeare? 

That time of year thou mayst in me behold 
When yellow leaves, or none, or few, do hang 
Upon those boughs which shake against the cold, 

Bare ruined choirs, where late the sweet birds sang. 

In me thou seest the twilight of such day 
As after sunset fadeth in the west, 

Which by and by black night doth take away, 

Death’s second self that seals up all in rest. 

In me thou seest the glowing of such fire 
That on the ashes of his youth doth lie, 

As the deathbed whereon it must expire 
Consumed with that which it was nourished by. 

This thou perceiv’st, which makes thy love more strong, 

To love that well which thou must leave ere long. 

In this sonnet, the poet uses extensive metaphors to describe his advancing 
age. His age is like late autumn, “when yellow leaves, or none, or few, do hang.” 
The weather is cold and the birds can no longer sit on the branches, which he 
calls “bare ruin’d choirs.” His age is like the twilight as the “sunset fadeth in the 
west, which by and by black night doth take away.” He is the remains of a fire 
“that on the ashes of his youth doth lie.” Indeed, all language is ultimately 
metaphor, though some expressions of it are more memorable than others. 

Finding a metaphor is the process of recognizing a pattern despite 
differences in detail and context—an activity we undertake trivially every 
moment of our lives. The metaphorical leaps that we consider of significance 
tend to take place in the interstices of different disciplines. Working against this 
essential force of creativity, however, is the pervasive trend toward ever greater 
specialization in the sciences (and just about every other field as well). As 
American mathematician Norbert Wiener (1894-1964) wrote in his seminal 
book Cybernetics, published the year I was born (1948): 

There are fields of scientific work, as we shall see in the body of this 
book, which have been explored from the different sides of pure 
mathematics, statistics, electrical engineering, and neurophysiology; in 
which every single notion receives a separate name from each group, and in 
which important work has been triplicated or quadruplicated, while still 
other important work is delayed by the unavailability in one field of results 
that may have already become classical in the next field. 

It is these boundary regions which offer the richest opportunities to the 
qualified investigator. They are at the same time the most refractory to the 
accepted techniques of mass attack and the division of labor. 

A technique I have used in my own work to combat increasing 
specialization is to assemble the experts that I have gathered for a project (for 
example, my speech recognition work included speech scientists, linguists, 
psychoacousticians, and pattern recognition experts, not to mention computer 
scientists) and encourage each one to teach the group his particular techniques 
and terminology. We then throw out all of that terminology and make up our 
own. Invariably we find metaphors from one field that solve problems in 

A mouse that finds an escape route when confronted with the household cat 
—and can do so even if the situation is somewhat different from what it has ever 
encountered before—is being creative. Our own creativity is orders of 
magnitude greater than that of the mouse—and involves far more levels of 
abstraction—because we have a much larger neocortex, which is capable of 
greater levels of hierarchy. So one way to achieve greater creativity is by 
effectively assembling more neocortex. 

One approach to expand the available neocortex is through the 
collaboration of multiple humans. This is accomplished routinely via the 
communication between people gathered in a problem-solving community. 
Recently there have been efforts to use online collaboration tools to harness the 
power of real-time collaboration, which have shown success in mathematics and 
other fields.- 

The next step, of course, will be to expand the neocortex itself with its 
nonbiological equivalent. This will be our ultimate act of creativity: to create the 
capability of being creative. A nonbiological neocortex will ultimately be faster 
and could rapidly search for the kinds of metaphors that inspired Darwin and 
Einstein. It could systematically explore all of the overlapping boundaries 
between our exponentially expanding frontiers of knowledge. 

Some people express concern about what will happen to those who would 

opt out of such mind expansion. I would point out that this additional 
intelligence will essentially reside in the cloud (the exponentially expanding 
network of computers that we connect to through online communication), where 
most of our machine intelligence is now stored. When you use a search engine, 
recognize speech from your phone, consult a virtual assistant such as Siri, or use 
your phone to translate a sign into another language, the intelligence is not in the 
device itself but in the cloud. Our expanded neocortex will be housed there too. 
Whether we access such expanded intelligence through direct neural connection 
or the way we do now—by interacting with it via our devices—is an arbitrary 
distinction. In my view we will all become more creative through this pervasive 
enhancement, whether we choose to opt in or out of direct connection to 
humanity’s expanded intelligence. We have already outsourced much of our 
personal, social, historical, and cultural memory to the cloud, and we will 
ultimately do the same thing with our hierarchical thinking. 

Einstein’s breakthrough resulted not only from his application of metaphors 
through mind experiments but also from his courage in believing in the power of 
those metaphors. He was willing to relinquish the traditional explanations that 
failed to satisfy his experiments, and he was willing to withstand the ridicule of 
his peers to the bizarre explanations that his metaphors implied. These qualities 
—belief in metaphor and courage of conviction—are ones that we should be able 
to program into our nonbiological neocortex as well. 


Clarity of mind means clarity of passion, too; this is why a great and clear 
mind loves ardently and sees distinctly what it loves. 

—Blaise Pascal 

There is always some madness in love. But there is also always some 
reason in madness. 

—Friedrich Nietzsche 

When you have seen as much of life as I have, you will not underestimate 
the power of obsessive love. 

—Albus Dumbledore, in J. K. Rowling, Harry Potter and the Half- 
Blood Prince 

I always like a good math solution to any love problem. 

—Michael Patrick King, from the “Take Me Out to the Ballgame” 
episode of Sex and the City 

If you haven’t actually experienced ecstatic love personally, you have 
undoubtedly heard about it. It is fair to say that a substantial fraction if not a 
majority of the world’s art—stories, novels, music, dance, paintings, television 
shows, and movies—is inspired by the stories of love in its earliest stages. 

Science has recently gotten into the act as well, and we are now able to 
identify the biochemical changes that occur when someone falls in love. 
Dopamine is released, producing feelings of happiness and delight. 
Norepinephrine levels soar, which lead to a racing heart and overall feelings of 
exhilaration. These chemicals, along with phenylethylamine, produce elation, 
high energy levels, focused attention, loss of appetite, and a general craving for 
the object of one’s desire. Interestingly, recent research at University College in 
London also shows that serotonin levels go down, similar to what happens in 
obsessive-compulsive disorder, which is consistent with the obsessive nature of 

early love.^ The high levels of dopamine and norepinephrine account for the 
heightened short-term attention, euphoria, and craving of early love. 

If these biochemical phenomena sound similar to those of the fight-or-flight 
syndrome, they are, except that here we are running toward something or 
someone; indeed, a cynic might say toward rather than away from danger. The 
changes are also fully consistent with those of the early phases of addictive 
behavior. The Roxy Music song “Love Is the Drug” is quite accurate in 
describing this state (albeit the subject of the song is looking to score his next fix 
of love). Studies of ecstatic religious experiences also show the same physical 
phenomena; it can be said that the person having such an experience is falling in 
love with God or whatever spiritual connection on which they are focused. 

In the case of early romantic love, estrogen and testosterone certainly play a 
role in establishing sex drive, but if sexual reproduction were the only 
evolutionary objective of love, then the romantic aspect of the process would not 
be necessary. As psychologist John William Money (1921-2006) wrote, “Lust is 
lewd, love is lyrical.” 

The ecstatic phase of love leads to the attachment phase and ultimately to a 
long-term bond. There are chemicals that encourage this process as well, 
including oxytocin and vasopressin. Consider two related species of voles: the 
prairie vole and the montane vole. They are pretty much identical, except that 
the prairie vole has receptors for oxytocin and vasopressin, whereas the montane 
vole does not. The prairie vole is noted for lifetime monogamous relationships, 
while the montane vole resorts almost exclusively to one-night stands. In the 
case of voles, the oxytocin and vasopressin receptors are pretty much 
determinative as to the nature of their love life. 

While these chemicals are influential on humans as well, our neocortex has 
taken a commanding role, as in everything else we do. Voles do have a 
neocortex, but it is postage-stamp sized and flat and just large enough for them 
to find a mate for life (or, in the case of montane voles, at least for the night) and 
carry out other basic vole behaviors. We humans have sufficient additional 
neocortex to engage in the expansive “lyrical” expressions to which Money 

From an evolutionary perspective, love itself exists to meet the needs of the 
neocortex. If we didn’t have a neocortex, then lust would be quite sufficient to 
guarantee reproduction. The ecstatic instigation of love leads to attachment and 
mature love, and results in a lasting bond. This in turn is designed to provide at 
least the possibility of a stable environment for children while their own 
neocortices undergo the critical learning needed to become responsible and 
capable adults. Learning in a rich environment is inherently part of the method 

of the neocortex. Indeed the same oxytocin and vasopressin hormone 
mechanisms play a key role in establishing the critical bonding of parent 
(especially mother) and child. 

At the far end of the story of love, a loved one becomes a major part of our 
neocortex. After decades of being together, a virtual other exists in the neocortex 
such that we can anticipate every step of what our lover will say and do. Our 
neocortical patterns are filled with the thoughts and patterns that reflect who they 
are. When we lose that person, we literally lose part of ourselves. This is not just 
a metaphor—all of the vast pattern recognizers that are filled with the patterns 
reflecting the person we love suddenly change their nature. Although they can be 
considered a precious way to keep that person alive within ourselves, the vast 
neocortical patterns of a lost loved one turn suddenly from triggers of delight to 
triggers of mourning. 

The evolutionary basis for love and its phases is not the full story in today’s 
world. We have already largely succeeded in liberating sex from its biological 
function, in that we can have babies without sex and we can certainly have sex 
without babies. The vast majority of sex takes place for its sensual and relational 
purposes. And we routinely fall in love for purposes other than raising children. 

Similarly, the vast expanse of artistic expression of all kinds that celebrates 
love and its myriad forms dating back to antiquity is also an end in itself. Our 
ability to create these enduring forms of transcendent knowledge—about love or 
anything else—is precisely what makes our species unique. 

The neocortex is biology’s greatest creation. In turn, it is the poems about 
love—and all of our other creations—that represent the greatest inventions of 
our neocortex. 



Never trust anything that can think for itself if you can’t see where it keeps 
its brain. 

—Arthur Weasley, in J. K. Rowling, Harry Potter and the Prisoner 
of Azkaban 

No, I’m not interested in developing a powerful brain. All I’m after is just a 
mediocre brain, something like the President of the American Telephone 
and Telegraph Company. 

—Alan Turing 

A computer would deserve to be called intelligent if it could deceive a 
human into believing that it was human. 

—Alan Turing 

I believe that at the end of the century the use of words and general 
educated opinion will have altered so much that one will be able to speak of 
machines thinking without expecting to be contradicted. 

—Alan Turing 

A mother rat will build a nest for her young even if she has never seen another 

rat in her lifetime.- Similarly, a spider will spin a web, a caterpillar will create 
her own cocoon, and a beaver will build a dam, even if no contemporary ever 

showed them how to accomplish these complex tasks. That is not to say that 
these are not learned behaviors. It is just that these animals did not learn them in 
a single lifetime—they learned them over thousands of lifetimes. The evolution 
of animal behavior does constitute a learning process, but it is learning by the 
species, not by the individual, and the fruits of this learning process are encoded 
in DNA. 

To appreciate the significance of the evolution of the neocortex, consider 
that it greatly sped up the process of learning (hierarchical knowledge) from 
thousands of years to months (or less). Even if millions of animals in a particular 
mammalian species failed to solve a problem (requiring a hierarchy of steps), it 
required only one to accidentally stumble upon a solution. That new method 
would then be copied and spread exponentially through the population. 

We are now in a position to speed up the learning process by a factor of 
thousands or millions once again by migrating from biological to nonbiological 
intelligence. Once a digital neocortex learns a skill, it can transfer that know¬ 
how in minutes or even seconds. As one of many examples, at my first company, 
Kurzweil Computer Products (now Nuance Speech Technologies), which I 
founded in 1973, we spent years training a set of research computers to 
recognize printed letters from scanned documents, a technology called omni-font 
(any type font) optical character recognition (OCR). This particular technology 
has now been in continual development for almost forty years, with the current 
product called OmniPage from Nuance. If you want your computer to recognize 
printed letters, you don’t need to spend years training it to do so, as we did—you 
can simply download the evolved patterns already learned by the research 
computers in the form of software. In the 1980s we began on speech recognition, 
and that technology, which has also been in continuous development now for 
several decades, is part of Siri. Again, you can download in seconds the evolved 
patterns learned by the research computers over many years. 

Ultimately we will create an artificial neocortex that has the full range and 
flexibility of its human counterpart. Consider the benefits. Electronic circuits are 
millions of times faster than our biological circuits. At first we will have to 
devote all of this speed increase to compensating for the relative lack of 
parallelism in our computers, but ultimately the digital neocortex will be much 
faster than the biological variety and will only continue to increase in speed. 

When we augment our own neocortex with a synthetic version, we won’t 
have to worry about how much additional neocortex can physically fit into our 
bodies and brains, as most of it will be in the cloud, like most of the computing 
we use today. I estimated earlier that we have on the order of 300 million pattern 
recognizers in our biological neocortex. That’s as much as could be squeezed 

into our skulls even with the evolutionary innovation of a large forehead and 
with the neocortex taking about 80 percent of the available space. As soon as we 
start thinking in the cloud, there will be no natural limits—we will be able to use 
billions or trillions of pattern recognizers, basically whatever we need, and 
whatever the law of accelerating returns can provide at each point in time. 

In order for a digital neocortex to learn a new skill, it will still require many 
iterations of education, just as a biological neocortex does, but once a single 
digital neocortex somewhere and at some time learns something, it can share that 
knowledge with every other digital neocortex without delay. We can each have 
our own private neocortex extenders in the cloud, just as we have our own 
private stores of personal data today. 

Last but not least, we will be able to back up the digital portion of our 
intelligence. As we have seen, it is not just a metaphor to state that there is 
information contained in our neocortex, and it is frightening to contemplate that 
none of this information is backed up today. There is, of course, one way in 
which we do back up some of the information in our brains—by writing it down. 
The ability to transfer at least some of our thinking to a medium that can outlast 
our biological bodies was a huge step forward, but a great deal of data in our 
brains continues to remain vulnerable. 

Brain Simulations 

One approach to building a digital brain is to simulate precisely a biological one. 
For example, Harvard brain sciences doctoral student David Dalrymple (born in 
1991) is planning to simulate the brain of a nematode (a roundworm).- 
Dalrymple selected the nematode because of its relatively simple nervous 
system, which consists of about 300 neurons, and which he plans to simulate at 
the very detailed level of molecules. He will also create a computer simulation of 
its body as well as its environment so that his virtual nematode can hunt for 
(virtual) food and do the other things that nematodes are good at. Dalrymple says 
it is likely to be the first complete brain upload from a biological animal to a 
virtual one that lives in a virtual world. Like his simulated nematode, whether 
even biological nematodes are conscious is open to debate, although in their 
struggle to eat, digest food, avoid predators, and reproduce, they do have 
experiences to be conscious of. 

At the opposite end of the spectmm, Henry Markram’s Blue Brain Project 
is planning to simulate the human brain, including the entire neocortex as well as 
the old-brain regions such as the hippocampus, amygdala, and cerebellum. His 
planned simulations will be built at varying degrees of detail, up to a full 
simulation at the molecular level. As I reported in chapter 4 . Markram has 
discovered a key module of several dozen neurons that is repeated over and over 
again in the neocortex, demonstrating that learning is done by these modules and 
not by individual neurons. 

Markram’s progress has been scaling up at an exponential pace. He 
simulated one neuron in 2005, the year the project was initiated. In 2008 his 
team simulated an entire neocortical column of a rat brain, consisting of 10,000 
neurons. By 2011 this expanded to 100 columns, totaling a million cells, which 
he calls a mesocircuit. One controversy concerning Markram’s work is how to 
verify that the simulations are accurate. In order to do this, these simulations will 
need to demonstrate learning that I discuss below. 

He projects simulating an entire rat brain of 100 mesocircuits, totaling 100 
million neurons and about a trillion synapses, by 2014. In a talk at the 2009 TED 
conference at Oxford, Markram said, “It is not impossible to build a human 
brain, and we can do it in 10 years.”- His most recent target for a full brain 
simulation is 2023.- 

Markram and his team are basing their model on detailed anatomical and 

electrochemical analyses of actual neurons. Using an automated device they 
created called a patch-clamp robot, they are measuring the specific ion channels, 
neurotransmitters, and enzymes that are responsible for the electrochemical 
activity within each neuron. Their automated system was able to do thirty years 
of analysis in six months, according to Markram. It was from these analyses that 
they noticed the “Lego memory” units that are the basic functional units of the 

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Actual and projected progress of the Blue Brain brain simulation 

Significant contributions to the technology of robotic patch-clamping was 
made by MIT neuroscientist Ed Boyden, Georgia Tech mechanical engineering 
professor Craig Forest, and Forest’s graduate student Suhasa Kodandaramaiah. 
They demonstrated an automated system with one-micrometer precision that can 
perform scanning of neural tissue at very close range without damaging the 
delicate membranes of the neurons. “This is something a robot can do that a 
human can’t,” Boyden commented. 

To return to Markram’s simulation, after simulating one neocortical column, 
Markram was quoted as saying, “Now we just have to scale it up.”- Scaling is 
certainly one big factor, but there is one other key hurdle, which is learning. If 
the Blue Brain Project brain is to “speak and have an intelligence and behave 
very much as a human does,” which is how Markram described his goal in a 

BBC interview in 2009, then it will need to have sufficient content in its 
simulated neocortex to perform those tasks.- As anyone who has tried to hold a 
conversation with a newborn can attest, there is a lot of learning that must be 
achieved before this is feasible. 

The tip of the patch-clamping robot developed at MIT and Georgia Tech 
scanning neural tissue. 

There are two obvious ways this can be done in a simulated brain such as 
Blue Brain. One would be to have the brain learn this content the way a human 
brain does. It can start out like a newborn human baby with an innate capacity 
for acquiring hierarchical knowledge and with certain transformations 
preprogrammed in its sensory preprocessing regions. But the learning that takes 
place between a biological infant and a human person who can hold a 
conversation would need to occur in a comparable manner in nonbiological 
learning. The problem with that approach is that a brain that is being simulated 
at the level of detail anticipated for Blue Brain is not expected to run in real time 
until at least the early 2020s. Even running in real time would be too slow unless 
the researchers are prepared to wait a decade or two to reach intellectual parity 
with an adult human, although real-time performance will get steadily faster as 
computers continue to grow in price/performance. 

The other approach is to take one or more biological human brains that 
have already gained sufficient knowledge to converse in meaningful language 

and to otherwise behave in a mature manner and copy their neocortical patterns 
into the simulated brain. The problem with this method is that it requires a 
noninvasive and nondestructive scanning technology of sufficient spatial and 
temporal resolution and speed to perform such a task quickly and completely. I 
would not expect such an “uploading” technology to be available until around 
the 2040s. (The computational requirement to simulate a brain at that degree of 
precision, which I estimate to be 10 19 calculations per second, will be available 
in a supercomputer according to my projections by the early 2020s; however, the 
necessary nondestructive brain scanning technologies will take longer.) 

There is a third approach, which is the one I believe simulation projects 
such as Blue Brain will need to pursue. One can simplify molecular models by 
creating functional equivalents at different levels of specificity, ranging from my 
own functional algorithmic method (as described in this book) to simulations 
that are closer to full molecular simulations. The speed of learning can thereby 
be increased by a factor of hundreds or thousands depending on the degree of 
simplification used. An educational program can be devised for the simulated 
brain (using the functional model) that it can learn relatively quickly. Then the 
full molecular simulation can be substituted for the simplified model while still 
using its accumulated learning. We can then simulate learning with the full 
molecular model at a much slower speed. 

American computer scientist Dharmendra Modha and his IBM colleagues 
have created a cell-by-cell simulation of a portion of the human visual neocortex 
comprising 1.6 billion virtual neurons and 9 trillion synapses, which is 
equivalent to a cat neocortex. It runs 100 times slower than real time on an IBM 
BlueGene/P supercomputer consisting of 147,456 processors. The work received 
the Gordon Bell Prize from the Association for Computing Machinery. 

The purpose of a brain simulation project such as Blue Brain and Modha’s 
neocortex simulations is specifically to refine and confirm a functional model. 
AI at the human level will principally use the type of functional algorithmic 
model discussed in this book. However, molecular simulations will help us to 
perfect that model and to fully understand which details are important. In my 
development of speech recognition technology in the 1980s and 1990s, we were 
able to refine our algorithms once the actual transformations performed by the 
auditory nerve and early portions of the auditory cortex were understood. Even if 
our functional model was perfect, understanding exactly how it is actually 
implemented in our biological brains will reveal important knowledge about 
human function and dysfunction. 

We will need detailed data on actual brains to create biologically based 
simulations. MarkranTs team is collecting its own data. There are large-scale 

projects to gather this type of data and make it generally available to scientists. 
For example, Cold Spring Harbor Laboratory in New York has collected 500 
terabytes of data by scanning a mammal brain (a mouse), which they made 
available in June 2012. Their project allows a user to explore a brain similarly to 
the way Google Earth allows one to explore the surface of the planet. You can 
move around the entire brain and zoom in to see individual neurons and their 
connections. You can highlight a single connection and then follow its path 
through the brain. 

Sixteen sections of the National Institutes of Health have gotten together 
and sponsored a major initiative called the Human Connectome Project with 
$38.5 million of funding.- Led by Washington University in St. Louis, the 
University of Minnesota, Harvard University, Massachusetts General Hospital, 
and the University of California at Los Angeles, the project seeks to create a 
similar three-dimensional map of connections in the human brain. The project is 
using a variety of noninvasive scanning technologies, including new forms of 
MRI, magnetoencephalography (measuring the magnetic fields produced by the 
electrical activity in the brain), and diffusion tractography (a method to trace the 
pathways of fiber bundles in the brain). As I point out in chapter 10 . the spatial 
resolution of noninvasive scanning of the brain is improving at an exponential 
rate. The research by Van J. Wedeen and his colleagues at Massachusetts 
General Hospital showing a highly regular gridlike structure of the wiring of the 
neocortex that I described in chapter 4 is one early result from this project. 

Oxford University computational neuroscientist Anders Sandberg (born in 
1972) and Swedish philosopher Nick Bostrom (born in 1973) have written the 
comprehensive Whole Brain Emulation: A Roadmap, which details the 
requirements for simulating the human brain (and other types of brains) at 
different levels of specificity from high-level functional models to simulating 
molecules.- The report does not provide a timeline, but it does describe the 
requirements to simulate different types of brains at varying levels of precision 
in terms of brain scanning, modeling, storage, and computation. The report 
projects ongoing exponential gains in all of these areas of capability and argues 
that the requirements to simulate the human brain at a high level of detail are 
coming into place. 


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and Nick Bostrom. 

Neural Nets 

In 1964, at the age of sixteen, I wrote to Frank Rosenblatt (1928-1971), a 
professor at Cornell University, inquiring about a machine called the Mark 1 
Perceptron. He had created it four years earlier, and it was described as having 
brainlike properties. He invited me to visit him and try the machine out. 

The Perceptron was built from what he claimed were electronic models of 
neurons. Input consisted of values arranged in two dimensions. For speech, one 
dimension represented frequency and the other time, so each value represented 
the intensity of a frequency at a given point in time. For images, each point was 
a pixel in a two-dimensional image. Each point of a given input was randomly 
connected to the inputs of the first layer of simulated neurons. Every connection 
had an associated synaptic strength, which represented its importance, and which 
was initially set at a random value. Each neuron added up the signals coming 
into it. If the combined signal exceeded a particular threshold, the neuron fired 
and sent a signal to its output connection; if the combined input signal did not 
exceed the threshold, the neuron did not fire, and its output was zero. The output 
of each neuron was randomly connected to the inputs of the neurons in the next 
layer. The Mark 1 Perceptron had three layers, which could be organized in a 
variety of configurations. For example, one layer might feed back to an earlier 
one. At the top layer, the output of one or more neurons, also randomly selected, 
provided the answer. (For an algorithmic description of neural nets, see this 

Since the neural net wiring and synaptic weights are initially set randomly, 
the answers of an untrained neural net are also random. The key to a neural net, 
therefore, is that it must learn its subject matter, just like the mammalian brains 
on which it’s supposedly modeled. A neural net starts out ignorant; its teacher— 
which may be a human, a computer program, or perhaps another, more mature 
neural net that has already learned its lessons—rewards the student neural net 
when it generates the correct output and punishes it when it does not. This 
feedback is in turn used by the student neural net to adjust the strength of each 
interneuronal connection. Connections that are consistent with the correct 
answer are made stronger. Those that advocate a wrong answer are weakened. 

Over time the neural net organizes itself to provide the correct answers 
without coaching. Experiments have shown that neural nets can learn their 
subject matter even with unreliable teachers. If the teacher is correct only 60 
percent of the time, the student neural net will still learn its lessons with an 
accuracy approaching 100 percent. 

However, limitations in the range of material that the Perceptron was 
capable of learning quickly became apparent. When I visited Professor 
Rosenblatt in 1964, I tried simple modifications to the input. The system was set 
up to recognize printed letters, and would recognize them quite accurately. It did 
a fairly good job of autoassociation (that is, it could recognize the letters even if 
I covered parts of them), but fared less well with invariance (that is, generalizing 
over size and font changes, which confused it). 

During the last half of the 1960s, these neural nets became enormously 
popular, and the field of “connectionism” took over at least half of the artificial 
intelligence field. The more traditional approach to AI, meanwhile, included 

direct attempts to program solutions to specific problems, such as how to 
recognize the invariant properties of printed letters. 

Another person I visited in 1964 was Marvin Minsky (born in 1927), one of 
the founders of the artificial intelligence field. Despite having done some 
pioneering work on neural nets himself in the 1950s, he was concerned with the 
great surge of interest in this technique. Part of the allure of neural nets was that 
they supposedly did not require programming—they would learn solutions to 
problems on their own. In 1965 I entered MIT as a student with Professor 
Minsky as my mentor, and I shared his skepticism about the craze for 

In 1969 Minsky and Seymour Papert (born in 1928), the two cofounders of 
the MIT Artificial Intelligence Laboratory, wrote a book called Perceptrons, 
which presented a single core theorem: specifically, that a Perceptron was 
inherently incapable of determining whether or not an image was connected. The 
book created a firestorm. Determining whether or not an image is connected is a 
task that humans can do very easily, and it is also a straightforward process to 
program a computer to make this discrimination. The fact that Perceptrons could 
not do so was considered by many to be a fatal flaw. 

Two images from the cover of the book Perceptrons by Marvin Minsky 
and Seymour Papert. The top image is not connected (that is, the dark area 
consists of two disconnected parts). The bottom image is connected. A 
human can readily determine this, as can a simple software program. A 
feedforward Perceptron such as Frank Rosenblatt’s Mark 1 Perceptron 
cannot make this determination. 

Perceptrons, however, was widely interpreted to imply more than it actually 
did. Minsky and Papert’s theorem applied only to a particular type of neural net 
called a feedforward neural net (a category that does include Rosenblatt’s 
Perceptron); other types of neural nets did not have this limitation. Still, the book 
did manage to largely kill most funding for neural net research during the 1970s. 
The field did return in the 1980s with attempts to use what were claimed to be 
more realistic models of biological neurons and ones that avoided the limitations 
implied by the Minsky-Papert Perceptron theorem. Nevertheless, the ability of 
the neocortex to solve the invariance problem, a key to its strength, was a skill 
that remained elusive for the resurgent connectionist field. 

Sparse Coding: Vector Quantization 

In the early 1980s I started a project devoted to another classical pattern 
recognition problem: understanding human speech. At first, we used traditional 
AI approaches by directly programming expert knowledge about the 
fundamental units of speech—phonemes—and rules from linguists on how 
people string phonemes together to form words and phrases. Each phoneme has 
distinctive frequency patterns. For example, we knew that vowels such as “e” 
and “ah” are characterized by certain resonant frequencies called formants, with 
a characteristic ratio of formants for each phoneme. Sibilant sounds such as “z” 
and “s” are characterized by a burst of noise that spans many frequencies. 

We captured speech as a waveform, which we then converted into multiple 
frequency bands (perceived as pitches) using a bank of frequency filters. The 
result of this transformation could be visualized and was called a spectrogram 
(see page 136 1. 

The filter bank is copying what the human cochlea does, which is the initial 
step in our biological processing of sound. The software first identified 
phonemes based on distinguishing patterns of frequencies and then identified 
words based on identifying characteristic sequences of phonemes. 

A spectrogram of three vowels. From left to right: [i] as in “appreciate,” 
[u] as in “acoustic,” and [a] as in “ah.” The Y axis represents frequency of 
sound. The darker the band the more acoustic energy there is at that 

A spectrogram of a person saying the word “hide.” The horizontal lines 
show the formants, which are sustained frequencies that have especially 
high energy.— 

The result was partially successful. We could train our device to learn the 
patterns for a particular person using a moderate-sized vocabulary, measured in 
thousands of words. When we attempted to recognize tens of thousands of 
words, handle multiple speakers, and allow fully continuous speech (that is, 
speech with no pauses between words), we ran into the invariance problem. 
Different people enunciated the same phoneme differently—for example, one 
person’s “e” phoneme may sound like someone else’s “ah.” Even the same 
person was inconsistent in the way she spoke a particular phoneme. The pattern 
of a phoneme was often affected by other phonemes nearby. Many phonemes 
were left out completely. The pronunciation of words (that is, how phonemes are 
strung together to form words) was also highly variable and dependent on 
context. The linguistic mles we had programmed were breaking down and could 
not keep up with the extreme variability of spoken language. 

It became clear to me at the time that the essence of human pattern and 
conceptual recognition was based on hierarchies. This is certainly apparent for 
human language, which constitutes an elaborate hierarchy of structures. But 
what is the element at the base of the structures? That was the first question I 
considered as I looked for ways to automatically recognize fully normal human 

Sound enters the ear as a vibration of the air and is converted by the 
approximately 3,000 inner hair cells in the cochlea into multiple frequency 
bands. Each hair cell is tuned to a particular frequency (note that we perceive 
frequencies as tones) and each acts as a frequency filter, emitting a signal 
whenever there is sound at or near its resonant frequency. As it leaves the human 
cochlea, sound is thereby represented by approximately 3,000 separate signals, 
each one signifying the time-varying intensity of a narrow band of frequencies 

(with substantial overlap among these bands). 

Even though it was apparent that the brain was massively parallel, it 
seemed impossible to me that it was doing pattern matching on 3,000 separate 
auditory signals. I doubted that evolution could have been that inefficient. We 
now know that very substantial data reduction does indeed take place in the 
auditory nerve before sound signals ever reach the neocortex. 

In our software-based speech recognizers, we also used filters implemented 
as software—sixteen to be exact (which we later increased to thirty-two, as we 
found there was not much benefit to going much higher than this). So in our 
system, each point in time was represented by sixteen numbers. We needed to 
reduce these sixteen streams of data into one while at the same emphasizing the 
features that are significant in recognizing speech. 

We used a mathematically optimal technique to accomplish this, called 
vector quantization. Consider that at any particular point in time, sound (at least 
from one ear) was represented by our software by sixteen different numbers: that 
is, the output of the sixteen frequency filters. (In the human auditory system the 
figure would be 3,000, representing the output of the 3,000 cochlea inner hair 
cells.) In mathematical terminology, each such set of numbers (whether 3,000 in 
the biological case or 16 in our software implementation) is called a vector. 

For simplicity, let’s consider the process of vector quantization with vectors 
of two numbers. Each vector can be considered a point in two-dimensional 


One vector consisting 
of 2 numbers 


If we have a very large sample of such vectors and plot them, we are likely 
to notice clusters forming. 


• • 

Multiple two-dimensional 
vectors forming 3 clusters 

• • • 

• •• 
• • • 
• • 


In order to identify the clusters, we need to decide how many we will allow. 
In our project we generally allowed 1,024 clusters so that we could number them 
and assign each cluster a 10-bit label (because 2 10 = 1,024). Our sample of 
vectors represents the diversity that we expect. We tentatively assign the first 
1,024 vectors to be one-point clusters. We then consider the 1,025th vector and 
find the point that it is closest to. If that distance is greater than the smallest 
distance between any pair of the 1,024 points, we consider it as the beginning of 
a new cluster. We then collapse the two (one-point) clusters that are closest 
together into a single cluster. We are thus still left with 1,024 clusters. After 
processing the 1,025th vector, one of those clusters now has more than one point. 
We keep processing points in this way, always maintaining 1,024 clusters. After 
we have processed all the points, we represent each multipoint cluster by the 
geometric center of the points in that cluster. 

• • • 

A cluster of points. We 
represent it using a 

single point that Is the 
geometric center of all 
the points in the cluster. 

We continue this iterative process until we have run through all the sample 
points. Typically we would process millions of points into 1,024 (2 10 ) clusters; 
we’ve also used 2,048 (2 11 ) or 4,096 (2 12 ) clusters. Each cluster is represented 
by one vector that is at the geometric center of all the points in that cluster. Thus 
the total of the distances of all the points in the cluster to the center point of the 
cluster is as small as possible. 

The result of this technique is that instead of having the millions of points 
that we started with (and an even larger number of possible points), we have now 
reduced the data to just 1,024 points that use the space of possibilities optimally. 
Parts of the space that are never used are not assigned any clusters. 

We then assign a number to each cluster (in our case, 0 to 1,023). That 
number is the reduced, “quantized” representation of that cluster, which is why 

the technique is called vector quantization. Any new input vector that arrives in 
the future is then represented by the number of the cluster whose center point is 
closest to this new input vector. 

We can now precompute a table with the distance of the center point of 
every cluster to every other center point. We thereby have instantly available the 
distance of this new input vector (which we represent by this quantized point— 
in other words, by the number of the cluster that this new point is closest to) to 
every other cluster. Since we are only representing points by their closest cluster, 
we now know the distance of this point to any other possible point that might 
come along. 

I described the technique above using vectors with only two numbers each, 
but working with sixteen-element vectors is entirely analogous to the simpler 
example. Because we chose vectors with sixteen numbers representing sixteen 
different frequency bands, each point in our system was a point in sixteen¬ 
dimensional space. It is difficult for us to imagine a space with more than three 
dimensions (perhaps four, if we include time), but mathematics has no such 

We have accomplished four things with this process. First, we have greatly 
reduced the complexity of the data. Second, we have reduced sixteen¬ 
dimensional data to one-dimensional data (that is, each sample is now a single 
number). Third, we have improved our ability to find invariant features, because 
we are emphasizing portions of the space of possible sounds that convey the 
most information. Most combinations of frequencies are physically impossible 
or at least very unlikely, so there is no reason to give equal space to unlikely 
combinations of inputs as to likely ones. This technique reduces the data to 
equally likely possibilities. The fourth benefit is that we can use one-dimensional 
pattern recognizers, even though the original data consisted of many more 
dimensions. This turned out to be the most efficient approach to utilizing 
available computational resources. 

Reading Your Mind with Hidden Markov Models 

With vector quantization, we simplified the data in a way that emphasized key 
features, but we still needed a way to represent the hierarchy of invariant 
features that would make sense of new information. Having worked in the field 
of pattern recognition at that time (the early 1980s) for twenty years, I knew that 
one-dimensional representations were far more powerful, efficient, and 
amenable to invariant results. There was not a lot known about the neocortex in 
the early 1980s, but based on my experience with a variety of pattern recognition 
problems, I assumed that the brain was also likely to be reducing its 
multidimensional data (whether from the eyes, the ears, or the skin) using a one¬ 
dimensional representation, especially as concepts rose in the neocortex’s 

For the speech recognition problem, the organization of information in the 
speech signal appeared to be a hierarchy of patterns, with each pattern 
represented by a linear string of elements with a forward direction. Each element 
of a pattern could be another pattern at a lower level, or a fundamental unit of 
input (which in the case of speech recognition would be our quantized vectors). 

You will recognize this situation as consistent with the model of the 
neocortex that I presented earlier. Human speech, therefore, is produced by a 
hierarchy of linear patterns in the brain. If we could simply examine these 
patterns in the brain of the person speaking, it would be a simple matter to match 
her new speech utterances against her brain patterns and understand what the 
person was saying. Unfortunately we do not have direct access to the brain of the 
speaker—the only information we have is what she actually said. Of course, that 
is the whole point of spoken language—the speaker is sharing a piece of her 
mind with her utterance. 

So I wondered: Was there a mathematical technique that would enable us to 
infer the patterns in the speaker’s brain based on her spoken words? One 
utterance would obviously not be sufficient, but if we had a large number of 
samples, could we use that information to essentially read the patterns in the 
speaker’s neocortex (or at least formulate something mathematically equivalent 
that would enable us to recognize new utterances)? 

People often fail to appreciate how powerful mathematics can be—keep in 
mind that our ability to search much of human knowledge in a fraction of a 
second with search engines is based on a mathematical technique. For the speech 

recognition problem I was facing in the early 1980s, it turned out that the 
technique of hidden Markov models fit the bill rather perfectly. The Russian 
mathematician Andrei Andreyevich Markov (1856-1922) built a mathematical 
theory of hierarchical sequences of states. The model was based on the 
possibility of traversing the states in one chain, and if that was successful, 
triggering a state in the next higher level in the hierarchy. Sound familiar? 

Pi,i P2.2 P3.3 P 4,4 

V ----w ^ ^ 

© P 12 P2 3 P3 4 P4 5 “* 

' Pi .3 P 2 ,4 p 3 5 - 

A simple example of one layer of a hidden Markov model. S x through 

S 4 represent the “hidden” internal states. The P f j transitions each represent 
the probability of going from state Sj to state Sj. These probabilities are 
determined by the system learning from training data (including during 
actual use). A new sequence (such as a new spoken utterance) is matched 
against these probabilities to determine the likelihood that this model 
produced the sequence. 

Markov’s model included probabilities of each state’s successfully 
occurring. He went on to hypothesize a situation in which a system has such a 
hierarchy of linear sequences of states, but those are unable to be directly 
examined—hence the name hidden Markov models. The lowest level of the 
hierarchy emits signals, which are all we are allowed to see. Markov provides a 
mathematical technique to compute what the probabilities of each transition 
must be based on the observed output. The method was subsequently refined by 
Norbert Wiener in 1923. Wiener’s refinement also provided a way to determine 
the connections in the Markov model; essentially any connection with too low a 
probability was considered not to exist. This is essentially how the human 
neocortex trims connections—if they are rarely or never used, they are 
considered unlikely and are pruned away. In our case, the observed output is the 
speech signal created by the person talking, and the state probabilities and 
connections of the Markov model constitute the neocortical hierarchy that 
produced it. 

I envisioned a system in which we would take samples of human speech, 
apply the hidden Markov model technique to infer a hierarchy of states with 

connections and probabilities (essentially a simulated neocortex for producing 
speech), and then use this inferred hierarchical network of states to recognize 
new utterances. To create a speaker-independent system, we would use samples 
from many different individuals to train the hidden Markov models. By adding 
in the element of hierarchies to represent the hierarchical nature of information 
in language, these were properly called hierarchical hidden Markov models 

My colleagues at Kurzweil Applied Intelligence were skeptical that this 
technique would work, given that it was a self-organizing method reminiscent of 
neural nets, which had fallen out of favor and with which we had had little 
success. I pointed out that the network in a neural net system is fixed and does 
not adapt to the input: The weights adapt, but the connections do not. In the 
Markov model system, if it was set up correctly, the system would prune unused 
connections so as to essentially adapt the topology. 

I established what was considered a “skunk works” project (an 
organizational term for a project off the beaten path that has little in the way of 
formal resources) that consisted of me, one part-time programmer, and an 
electrical engineer (to create the frequency filter bank). To the surprise of my 
colleagues, our effort turned out to be very successful, having succeeded in 
recognizing speech comprising a large vocabulary with high accuracy. 

After that experiment, all of our subsequent speech recognition efforts have 
been based on hierarchical hidden Markov models. Other speech recognition 
companies appeared to discover the value of this method independently, and 
since the mid-1980s most work in automated speech recognition has been based 
on this approach. Hidden Markov models are also used in speech synthesis— 
keep in mind that our biological cortical hierarchy is used not only to recognize 
input but also to produce output, for example, speech and physical movement. 

HHMMs are also used in systems that understand the meaning of natural- 
language sentences, which represents going up the conceptual hierarchy. 

Markov states 

Actual natural language 



Hidden Markov states and possible transitions to produce a sequence of 
words in natural-language text. 

To understand how the HHMM method works, we start out with a network 
that consists of all the state transitions that are possible. The vector quantization 
method described above is critical here, because otherwise there would be too 
many possibilities to consider. 

Here is a possible simplified initial topology: 

“one” - W-AX-N 


HMM for 


HMM for 


HMM for 

“ M ” 



“two" — T-OO 

Entry node 

/ \ / y 

\ ▼ \ ▼ 

Exit node 


HMM for HMM for 

“T” “00” 

A simple hidden Markov model topology to recognize two spoken 

Sample utterances are processed one by one. For each, we iteratively 
modify the probabilities of the transitions to better reflect the input sample we 
have just processed. The Markov models used in speech recognition code the 
likelihood that specific patterns of sound are found in each phoneme, how the 
phonemes influence one another, and the likely orders of phonemes. The system 
can also include probability networks on higher levels of language structure, 
such as the order of words, the inclusion of phrases, and so on up the hierarchy 
of language. 

Whereas our previous speech recognition systems incorporated specific 
rules about phoneme structures and sequences explicitly coded by human 
linguists, the new HHMM-based system was not explicitly told that there are 
forty-four phonemes in English, the sequences of vectors that were likely for 
each phoneme, or what phoneme sequences were more likely than others. We let 
the system discover these “rules” for itself from thousands of hours of 
transcribed human speech data. The advantage of this approach over hand-coded 
rules is that the models develop probabilistic rules of which human experts are 
often not aware. We noticed that many of the rules that the system had 
automatically learned from the data differed in subtle but important ways from 
the rules established by human experts. 

Once the network was trained, we began to attempt to recognize speech by 
considering the alternate paths through the network and picking the path that was 
most likely, given the actual sequence of input vectors we had seen. In other 
words, if we saw a sequence of states that was likely to have produced that 
utterance, we concluded that the utterance came from that cortical sequence. 
This simulated HHMM-based neocortex included word labels, so it was able to 
propose a transcription of what it heard. 

We were then able to improve our results further by continuing to train the 
network while we were using it for recognition. As we have discussed, 
simultaneous recognition and learning also take place at every level in our 
biological neocortical hierarchy. 

Evolutionary (Genetic) Algorithms 

There is another important consideration: How do we set the many parameters 
that control a pattern recognition system’s functioning? These could include the 
number of vectors that we allow in the vector quantization step, the initial 
topology of hierarchical states (before the training phase of the hidden Markov 
model process prunes them back), the recognition threshold at each level of the 
hierarchy, the parameters that control the handling of the size parameters, and 
many others. We can establish these based on our intuition, but the results will 
be far from optimal. 

We call these parameters “God parameters” because they are set prior to the 
self-organizing method of determining the topology of the hidden Markov 
models (or, in the biological case, before the person learns her lessons by 
similarly creating connections in her cortical hierarchy). This is perhaps a 
misnomer, given that these initial DNA-based design details are determined by 
biological evolution, though some may see the hand of God in that process (and 
while I do consider evolution to be a spiritual process, this discussion properly 
belongs in chapter 9 ). 

When it came to setting these “God parameters” in our simulated 
hierarchical learning and recognizing system, we again took a cue from nature 
and decided to evolve them—in our case, using a simulation of evolution. We 
used what are called genetic or evolutionary algorithms (GAs), which include 
simulated sexual reproduction and mutations. 

Here is a simplified description of how this method works. First, we 
determine a way to code possible solutions to a given problem. If the problem is 
optimizing the design parameters for a circuit, then we define a list of all of the 
parameters (with a specific number of bits assigned to each parameter) that 
characterize the circuit. This list is regarded as the genetic code in the genetic 
algorithm. Then we randomly generate thousands or more genetic codes. Each 
such genetic code (which represents one set of design parameters) is considered 
a simulated “solution” organism. 

Now we evaluate each simulated organism in a simulated environment by 
using a defined method to assess each set of parameters. This evaluation is a key 
to the success of a genetic algorithm. In our example, we would run each 
program generated by the parameters and judge it on appropriate criteria (did it 
complete the task, how long did it take, and so on). The best-solution organisms 

(the best designs) are allowed to survive, and the rest are eliminated. 

Now we cause each of the survivors to multiply themselves until they reach 
the same number of solution creatures. This is done by simulating sexual 
reproduction: In other words, we create new offspring where each new creature 
draws one part of its genetic code from one parent and another part from a 
second parent. Usually no distinction is made between male or female 
organisms; it’s sufficient to generate an offspring from any two arbitrary parents, 
so we’re basically talking about same-sex marriage here. This is perhaps not as 
interesting as sexual reproduction in the natural world, but the relevant point 
here is having two parents. As these simulated organisms multiply, we allow 
some mutation (random change) in the chromosomes to occur. 

We’ve now defined one generation of simulated evolution; now we repeat 
these steps for each subsequent generation. At the end of each generation we 
determine how much the designs have improved (that is, we compute the 
average improvement in the evaluation function over all the surviving 
organisms). When the degree of improvement in the evaluation of the design 
creatures from one generation to the next becomes very small, we stop this 
iterative cycle and use the best design(s) in the last generation. (For an 
algorithmic description of genetic algorithms, see this endnote.)— 

The key to a genetic algorithm is that the human designers don’t directly 
program a solution; rather, we let one emerge through an iterative process of 
simulated competition and improvement. Biological evolution is smart but slow, 
so to enhance its intelligence we greatly speed up its ponderous pace. The 
computer is fast enough to simulate many generations in a matter of hours or 
days, and we’ve occasionally had them run for as long as weeks to simulate 
hundreds of thousands of generations. But we have to go through this iterative 
process only once; as soon as we have let this simulated evolution run its course, 
we can apply the evolved and highly refined rules to real problems in a rapid 
fashion. In the case of our speech recognition systems, we used them to evolve 
the initial topology of the network and other critical parameters. We thus used 
two self-organizing methods: a GA to simulate the biological evolution that gave 
rise to a particular cortical design, and HHMMs to simulate the cortical 
organization that accompanies human learning. 

Another major requirement for the success of a GA is a valid method of 
evaluating each possible solution. This evaluation needs to be conducted quickly, 
because it must take account of many thousands of possible solutions for each 
generation of simulated evolution. GAs are adept at handling problems with too 
many variables for which to compute precise analytic solutions. The design of an 
engine, for example, may involve more than a hundred variables and requires 

satisfying dozens of constraints; GAs used by researchers at General Electric 
were able to come up with jet engine designs that met the constraints more 
precisely than conventional methods. 

When using GAs you must, however, be careful what you ask for. A genetic 
algorithm was used to solve a block-stacking problem, and it came up with a 
perfect solution...except that it had thousands of steps. The human programmers 
forgot to include minimizing the number of steps in their evaluation function. 

Scott Drave’s Electric Sheep project is a GA that produces art. The 
evaluation function uses human evaluators in an open-source collaboration 
involving many thousands of people. The art moves through time and you can 
view it at 

For speech recognition, the combination of genetic algorithms and hidden 
Markov models worked extremely well. Simulating evolution with a GA was 
able to substantially improve the performance of the HHMM networks. What 
evolution came up with was far superior to our original design, which was based 
on our intuition. 

We then experimented with introducing a series of small variations in the 
overall system. For example, we would make perturbations (minor random 
changes) to the input. Another such change was to have adjacent Markov models 
“leak” into one another by causing the results of one Markov model to influence 
models that are “nearby.” Although we did not realize it at the time, the sorts of 
adjustments we were experimenting with are very similar to the types of 
modifications that occur in biological cortical structures. 

At first, such changes hurt performance (as measured by accuracy of 
recognition). But if we reran evolution (that is, reran the GA) with these 
alterations in place, it would adapt the system accordingly, optimizing it for 
these introduced modifications. In general, this would restore performance. If we 
then removed the changes we had introduced, performance would be again 
degraded, because the system had been evolved to compensate for the changes. 
The adapted system became dependent on the changes. 

One type of alteration that actually helped performance (after rerunning the 
GA) was to introduce small random changes to the input. The reason for this is 
the well-known “overfitting” problem in self-organizing systems. There is a 
danger that such a system will overgeneralize to the specific examples contained 
in the training sample. By making random adjustments to the input, the more 
invariant patterns in the data survive, and the system thereby learns these deeper 
patterns. This helped only if we reran the GA with the randomization feature on. 

This introduces a dilemma in our understanding of our biological cortical 
circuits. It had been noticed, for example, that there might indeed be a small 

amount of leakage from one cortical connection to another, resulting from the 
way that biological connections are formed: The electrochemistry of the axons 
and dendrites is apparently subject to the electromagnetic effects of nearby 
connections. Suppose we were able to run an experiment where we removed this 
effect in an actual brain. That would be difficult to actually carry out, but not 
necessarily impossible. Suppose we conducted such an experiment and found 
that the cortical circuits worked less effectively without this neural leakage. We 
might then conclude that this phenomenon was a very clever design by evolution 
and was critical to the cortex’s achieving its level of performance. We might 
further point out that such a result shows that the orderly model of the flow of 
patterns up the conceptual hierarchy and the flow of predictions down the 
hierarchy was in fact much more complicated because of this intricate influence 
of connections on one another. 

But that would not necessarily be an accurate conclusion. Consider our 
experience with a simulated cortex based on HHMMs, in which we implemented 
a modification very similar to interneuronal cross talk. If we then ran evolution 
with that phenomenon in place, performance would be restored (because the 
evolutionary process adapted to it). If we then removed the cross talk, 
performance would be compromised again. In the biological case, evolution (that 
is, biological evolution) was indeed “run” with this phenomenon in place. The 
detailed parameters of the system have thereby been set by biological evolution 
to be dependent on these factors, so that changing them will negatively affect 
performance unless we run evolution again. Doing so is feasible in the simulated 
world, where evolution only takes days or weeks, but in the biological world it 
would require tens of thousands of years. 

So how can we tell whether a particular design feature of the biological 
neocortex is a vital innovation introduced by biological evolution—that is, one 
that is instrumental to our level of intelligence—or merely an artifact that the 
design of the system is now dependent on but could have evolved without? We 
can answer that question simply by running simulated evolution with and 
without these particular variations to the details of the design (for example, with 
and without connection cross talk). We can even do so with biological evolution 
if we’re examining the evolution of a colony of microorganisms where 
generations are measured in hours, but it is not practical for complex organisms 
such as humans. This is another one of the many disadvantages of biology. 

Getting back to our work in speech recognition, we found that if we ran 
evolution (that is, a GA) separately on the initial design of (1) the hierarchical 
hidden Markov models that were modeling the internal structure of phonemes 
and (2) the HHMMs’ modeling of the structures of words and phrases, we got 

even better results. Both levels of the system were using HHMMs, but the GA 
would evolve design variations between these different levels. This approach 
still allowed the modeling of phenomena that occurs in between the two levels, 
such as the smearing of phonemes that often happens when we string certain 
words together (for example, “How are you all doing?” might become “How’re 
y’all doing?”). 

It is likely that a similar phenomenon took place in different biological 
cortical regions, in that they have evolved small differences based on the types 
of patterns they deal with. Whereas all of these regions use the same essential 
neocortical algorithm, biological evolution has had enough time to fine-tune the 
design of each of them to be optimal for their particular patterns. However, as I 
discussed earlier, neuroscientists and neurologists have noticed substantial 
plasticity in these areas, which supports the idea of a general neocortical 
algorithm. If the fundamental methods in each region were radically different, 
then such interchangeability among cortical regions would not be possible. 

The systems we created in our research using this combination of self¬ 
organizing methods were very successful. In speech recognition, they were able 
for the first time to handle fully continuous speech and relatively unrestricted 
vocabularies. We were able to achieve a high accuracy rate on a wide variety of 
speakers, accents, and dialects. The current state of the art as this book is being 
written is represented by a product called Dragon Naturally Speaking (Version 
11.5) for the PC from Nuance (formerly Kurzweil Computer Products). I suggest 
that people try it if they are skeptical about the performance of contemporary 
speech recognition—accuracies are often 99 percent or higher after a few 
minutes of training on your voice on continuous speech and relatively 
unrestricted vocabularies. Dragon Dictation is a simpler but still impressive free 
app for the iPhone that requires no voice training. Siri, the personal assistant on 
contemporary Apple iPhones, uses the same speech recognition technology with 
extensions to handle natural-language understanding. 

The performance of these systems is a testament to the power of 
mathematics. With them we are essentially computing what is going on in the 
neocortex of a speaker—even though we have no direct access to that person’s 
brain—as a vital step in recognizing what the person is saying and, in the case of 
systems like Siri, what those utterances mean. We might wonder, if we were to 
actually look inside the speaker’s neocortex, would we see connections and 
weights corresponding to the hierarchical hidden Markov models computed by 
the software? Almost certainly we would not find a precise match; the neuronal 
structures would invariably differ in many details compared with the models in 
the computer. However, I would maintain that there must be an essential 

mathematical equivalence to a high degree of precision between the actual 
biology and our attempt to emulate it; otherwise these systems would not work 
as well as they do. 


LISP (LISt Processor) is a computer language, originally specified by AI pioneer 
John McCarthy (1927-2011) in 1958. As its name suggests, LISP deals with 
lists. Each LISP statement is a list of elements; each element is either another list 
or an “atom,” which is an irreducible item constituting either a number or a 
symbol. A list included in a list can be the list itself, hence LISP is capable of 
recursion. Another way that LISP statements can be recursive is if a list includes 
a list, and so on until the original list is specified. Because lists can include lists, 
LISP is also capable of hierarchical processing. A list can be a conditional such 
that it only “fires” if its elements are satisfied. In this way, hierarchies of such 
conditionals can be used to identify increasingly abstract qualities of a pattern. 

LISP became the rage in the artificial intelligence community in the 1970s 
and early 1980s. The conceit of the LISP enthusiasts of the earlier decade was 
that the language mirrored the way the human brain worked—that any intelligent 
process could most easily and efficiently be coded in LISP. There followed a 
mini-boomlet in “artificial intelligence” companies that offered LISP interpreters 
and related LISP products, but when it became apparent in the mid-1980s that 
LISP itself was not a shortcut to creating intelligent processes, the investment 
balloon collapsed. 

It turns out that the LISP enthusiasts were not entirely wrong. Essentially, 
each pattern recognizer in the neocortex can be regarded as a LISP statement— 
each one constitutes a list of elements, and each element can be another list. The 
neocortex is therefore indeed engaged in list processing of a symbolic nature 
very similar to that which takes place in a LISP program. Moreover, it processes 
all 300 million LISP-like “statements” simultaneously. 

However, there were two important features missing from the world of 
LISP, one of which was learning. LISP programs had to be coded line by line by 
human programmers. There were attempts to automatically code LISP programs 
using a variety of methods, but these were not an integral part of the language’s 
concept. The neocortex, in contrast, programs itself, filling its “statements” (that 
is, the lists) with meaningful and actionable information from its own experience 
and from its own feedback loops. This is a key principle of how the neocortex 
works: Each one of its pattern recognizers (that is, each LISP-like statement) is 
capable of filling in its own list and connecting itself both up and down to other 
lists. The second difference is the size parameters. One could create a variant of 

LISP (coded in LISP) that would allow for handling such parameters, but these 
are not part of the basic language. 

LISP is consistent with the original philosophy of the AI field, which was to 
find intelligent solutions to problems and to code them directly in computer 
languages. The first attempt at a self-organizing method that would teach itself 
from experience—neural nets—was not successful because it did not provide a 
means to modify the topology of the system in response to learning. The 
hierarchical hidden Markov model effectively provided that through its pruning 
mechanism. Today, the HHMM together with its mathematical cousins makes up 
a major portion of the world of AI. 

A corollary of the observation of the similarity of LISP and the list structure 
of the neocortex is an argument made by those who insist that the brain is too 
complicated for us to understand. These critics point out that the brain has 
trillions of connections, and since each one must be there specifically by design, 
they constitute the equivalent of trillions of lines of code. As we’ve seen, I’ve 
estimated that there are on the order of 300 million pattern processors in the 
neocortex—or 300 million lists where each element in the list is pointing to 
another list (or, at the lowest conceptual level, to a basic irreducible pattern from 
outside the neocortex). But 300 million is still a reasonably big number of LISP 
statements and indeed is larger than any human-written program in existence. 

However, we need to keep in mind that these lists are not actually specified 
in the initial design of the nervous system. The brain creates these lists itself and 
connects the levels automatically from its own experiences. This is the key 
secret of the neocortex. The processes that accomplish this self-organization are 
much simpler than the 300 million statements that constitute the capacity of the 
neocortex. Those processes are specified in the genome. As I will demonstrate in 
chapter 11 . the amount of unique information in the genome (after lossless 
compression) as applied to the brain is about 25 million bytes, which is 
equivalent to less than a million lines of code. The actual algorithmic complexity 
is even less than that, as most of the 25 million bytes of genetic information 
pertain to the biological needs of the neurons, and not specifically to their 
information-processing capability. However, even 25 million bytes of design 
information is a level of complexity we can handle. 

Hierarchical Memory Systems 

As I discussed in chapter 3 . Jeff Hawkins and Dileep George in 2003 and 2004 
developed a model of the neocortex incorporating hierarchical lists that was 
described in Hawkins and Blakeslee’s 2004 book On Intelligence. A more up-to- 
date and very elegant presentation of the hierarchical temporal memory method 
can be found in Dileep George’s 2008 doctoral dissertation.— Numenta has 
implemented it in a system called NuPIC (Numenta Platform for Intelligent 
Computing) and has developed pattern recognition and intelligent data-mining 
systems for such clients as Forbes and Power Analytics Corporation. After 
working at Numenta, George has started a new company called Vicarious 
Systems with funding from the Founder Fund (managed by Peter Thiel, the 
venture capitalist behind Facebook, and Sean Parker, the first president of 
Facebook) and from Good Ventures, led by Dustin Moskovitz, cofounder of 
Facebook. George reports significant progress in automatically modeling, 
learning, and recognizing information with a substantial number of hierarchies. 
He calls his system a “recursive cortical network” and plans applications for 
medical imaging and robotics, among other fields. The technique of hierarchical 
hidden Markov models is mathematically very similar to these hierarchical 
memory systems, especially if we allow the HHMM system to organize its own 
connections between pattern recognition modules. As mentioned earlier, 
HHMMs provide for an additional important element, which is modeling the 
expected distribution of the magnitude (on some continuum) of each input in 
computing the probability of the existence of the pattern under consideration. I 
have recently started a new company called Patterns, Inc., which intends to 
develop hierarchical self-organizing neocortical models that utilize HHMMs and 
related techniques for the purpose of understanding natural language. An 
important emphasis will be on the ability for the system to design its own 
hierarchies in a manner similar to a biological neocortex. Our envisioned system 
will continually read a wide range of material such as Wikipedia and other 
knowledge resources as well as listen to everything you say and watch 
everything you write (if you let it). The goal is for it to become a helpful friend 
answering your questions —before you even formulate them—and giving you 
useful information and tips as you go through your day. 

The Moving Frontier of AI: Climbing the Competence 

1. A long tiresome speech delivered by a frothy pie topping. 

2. A garment worn by a child, perhaps aboard an operatic ship. 

3. Wanted for a twelve-year crime spree of eating King Hrothgar’s 
warriors; officer Beowulf has been assigned the case. 

4. It can mean to develop gradually in the mind or to carry during 

5. National Teacher Day and Kentucky Derby Day. 

6. Wordsworth said they soar but never roam. 

7. Four-letter word for the iron fitting on the hoof of a horse or a card¬ 
dealing box in a casino. 

8. In act three of an 1846 Verdi opera, this Scourge of God is stabbed to 
death by his lover, Odabella. 

—Examples of Jeopardy! queries, all of which Watson got correct. 
Answers are: meringue harangue, pinafore, Grendel, gestate. 
May, skylark, shoe. For the eighth query, Watson replied, 
“What is Attila?” The host responded by saying, “Be more 
specific?” Watson clarified with, “What is Attila the Hun?,” 
which is correct. 

The computer’s techniques for unraveling Jeopardy! clues sounded just like 
mine. That machine zeroes in on key words in a clue, then combs its 
memory (in Watson’s case, a 15-terabyte data bank of human knowledge) 
for clusters of associations with these words. It rigorously checks the top 
hits against all the contextual information it can muster: the category name; 
the kind of answer being sought; the time, place, and gender hinted at in the 
clue; and so on. And when it feels “sure” enough, it decides to buzz. This is 
all an instant, intuitive process for a human Jeopardy! player, but I felt 
convinced that under the hood my brain was doing more or less the same 

—Ken Jennings, human Jeopardy! champion who lost to Watson 

I, for one, welcome our new robot overlords. 

—Ken Jennings, paraphrasing The Simpsons, after losing to 

Oh my god. [Watson] is more intelligent than the average Jeopardy! player 
in answering Jeopardy! questions. That’s impressively intelligent. 

—Sebastian Thrun, former director of the Stanford AI Lab 

Watson understands nothing. It’s a bigger steamroller. 

—Noam Chomsky 

Artificial intelligence is all around us—we no longer have our hand on the plug. 
The simple act of connecting with someone via a text message, e-mail, or cell 
phone call uses intelligent algorithms to route the information. Almost every 
product we touch is originally designed in a collaboration between human and 
artificial intelligence and then built in automated factories. If all the AI systems 
decided to go on strike tomorrow, our civilization would be crippled: We 
couldn’t get money from our bank, and indeed, our money would disappear; 
communication, transportation, and manufacturing would all grind to a halt. 
Fortunately, our intelligent machines are not yet intelligent enough to organize 
such a conspiracy. 

What is new in AI today is the viscerally impressive nature of publicly 
available examples. For example, consider Google’s self-driving cars (which as 
of this writing have gone over 200,000 miles in cities and towns), a technology 
that will lead to significantly fewer crashes, increased capacity of roads, 
alleviating the requirement of humans to perform the chore of driving, and many 
other benefits. Driverless cars are actually already legal to operate on public 
roads in Nevada with some restrictions, although widespread usage by the public 
throughout the world is not expected until late in this decade. Technology that 
intelligently watches the road and warns the driver of impending dangers is 
already being installed in cars. One such technology is based in part on the 
successful model of visual processing in the brain created by MIT’s Tomaso 
Poggio. Called MobilEye, it was developed by Amnon Shashua, a former 
postdoctoral student of Poggio’s. It is capable of alerting the driver to such 
dangers as an impending collision or a child running in front of the car and has 

recently been installed in cars by such manufacturers as Volvo and BMW. 

I will focus in this section of the book on language technologies for several 
reasons. Not surprisingly, the hierarchical nature of language closely mirrors the 
hierarchical nature of our thinking. Spoken language was our first technology, 
with written language as the second. My own work in artificial intelligence, as 
this chapter has demonstrated, has been heavily focused on language. Finally, 
mastering language is a powerfully leveraged capability. Watson has already 
read hundreds of millions of pages on the Web and mastered the knowledge 
contained in these documents. Ultimately machines will be able to master all of 
the knowledge on the Web—which is essentially all of the knowledge of our 
human-machine civilization. 

English mathematician Alan Turing (1912-1954) based his eponymous test 
on the ability of a computer to converse in natural language using text 
messages.— Turing felt that all of human intelligence was embodied and 
represented in language, and that no machine could pass a Turing test through 
simple language tricks. Although the Turing test is a game involving written 
language, Turing believed that the only way that a computer could pass it would 
be for it to actually possess the equivalent of human-level intelligence. Critics 
have proposed that a true test of human-level intelligence should include mastery 
of visual and auditory information as well.— Since many of my own AI projects 
involve teaching computers to master such sensory information as human 
speech, letter shapes, and musical sounds, I would be expected to advocate the 
inclusion of these forms of information in a true test of intelligence. Yet I agree 
with Turing’s original insight that the text-only version of the Turing test is 
sufficient. Adding visual or auditory input or output to the test would not 
actually make it more difficult to pass. 

One does not need to be an AI expert to be moved by the performance of 
Watson on Jeopardy! Although I have a reasonable understanding of the 
methodology used in a number of its key subsystems, that does not diminish my 
emotional reaction to watching it— him? —perform. Even a perfect 
understanding of how ah of its component systems work—which no one actually 
has—would not help you to predict how Watson would actually react to a given 
situation. It contains hundreds of interacting subsystems, and each of these is 
considering millions of competing hypotheses at the same time, so predicting the 
outcome is impossible. Doing a thorough analysis—after the fact—of Watson’s 
deliberations for a single three-second query would take a human centuries. 

To continue my own history, in the late 1980s and 1990s we began working 
on natural-language understanding in limited domains. You could speak to one 

of our products, called Kurzweil Voice, about anything you wanted, so long as it 
had to do with editing documents. (For example, “Move the third paragraph on 
the previous page to here.”) It worked pretty well in this limited but useful 
domain. We also created systems with medical domain knowledge so that 
doctors could dictate patient reports. It had enough knowledge of fields such as 
radiology and pathology that it could question the doctor if something in the 
report seemed unclear, and would guide the physician through the reporting 
process. These medical reporting systems have evolved into a billion-dollar 
business at Nuance. 

Understanding natural language, especially as an extension to automatic 
speech recognition, has now entered the mainstream. As of the writing of this 
book, Siri, the automated personal assistant on the iPhone 4S, has created a stir 
in the mobile computing world. You can pretty much ask Siri to do anything that 
a self-respecting smartphone should be capable of doing (for example, “Where 
can I get some Indian food around here?” or “Text my wife that I’m on my way,” 
or “What do people think of the new Brad Pitt movie?”), and most of the time 
Siri will comply. Siri will entertain a small amount of nonproductive chatter. If 
you ask her what the meaning of life is, she will respond with “42,” which fans 
of The Hitchhiker’s Guide to the Galaxy will recognize as its “answer to the 
ultimate question of life, the universe, and everything.” Knowledge questions 
(including the one about the meaning of life) are answered by Wolfram Alpha, 
described on page 170 . There is a whole world of “chatbots” who do nothing but 
engage in small talk. If you would like to talk to our chatbot named Ramona, go 
to our Web site and click on “Chat with Ramona.” 

Some people have complained to me about Siri’s failure to answer certain 
requests, but I often recall that these are the same people who persistently 
complain about human service providers also. I sometimes suggest that we try it 
together, and often it works better than they expect. The complaints remind me 
of the story of the dog who plays chess. To an incredulous questioner, the dog’s 
owner replies, “Yeah, it’s true, he does play chess, but his endgame is weak.” 
Effective competitors are now emerging, such as Google Voice Search. 

That the general public is now having conversations in natural spoken 
language with their handheld computers marks a new era. It is typical that people 
dismiss the significance of a first-generation technology because of its 
limitations. A few years later, when the technology does work well, people still 
dismiss its importance because, well, it’s no longer new. That being said, Siri 
works impressively for a first-generation product, and it is clear that this 
category of product is only going to get better. 

Siri uses the HMM-based speech recognition technologies from Nuance. 

The natural-language extensions were first developed by the DARPA-funded 
“CALO” project.— Siri has been enhanced with Nuance’s own natural-language 
technologies, and Nuance offers a very similar technology called Dragon Go!— 

The methods used for understanding natural language are very similar to 
hierarchical hidden Markov models, and indeed HHMM itself is commonly 
used. Whereas some of these systems are not specifically labeled as using HMM 
or HHMM, the mathematics is virtually identical. They all involve hierarchies of 
linear sequences where each element has a weight, connections that are self- 
adapting, and an overall system that self-organizes based on learning data. 
Usually the learning continues during actual use of the system. This approach 
matches the hierarchical structure of natural language—it is just a natural 
extension up the conceptual ladder from parts of speech to words to phrases to 
semantic structures. It would make sense to run a genetic algorithm on the 
parameters that control the precise learning algorithm of this class of hierarchical 
learning systems and determine the optimal algorithmic details. 

Over the past decade there has been a shift in the way that these hierarchical 
structures are created. In 1984 Douglas Lenat (born in 1950) started the 
ambitious Cyc (for enCYClopedic) project, which aimed to create rules that 
would codify everyday “commonsense” knowledge. The rules were organized in 
a huge hierarchy, and each rule involved—again—a linear sequence of states. 
For example, one Cyc rule might state that a dog has a face. Cyc can then link to 
general rules about the structure of faces: that a face has two eyes, a nose, and a 
mouth, and so on. We don’t need to have one set of rules for a dog’s face and 
then another for a cat’s face, though we may of course want to put in additional 
rules for ways in which dogs’ faces differ from cats’ faces. The system also 
includes an inference engine: If we have rules that state that a cocker spaniel is a 
dog, that dogs are animals, and that animals eat food, and if we were to ask the 
inference engine whether cocker spaniels eat, the system would respond that yes, 
cocker spaniels eat food. Over the next twenty years, and with thousands of 
person-years of effort, over a million such rules were written and tested. 
Interestingly, the language for writing Cyc rules—called CycL—is almost 
identical to LISP. 

Meanwhile, an opposing school of thought believed that the best approach 
to natural-language understanding, and to creating intelligent systems in general, 
was through automated learning from exposure to a very large number of 
instances of the phenomena the system was trying to master. A powerful 
example of such a system is Google Translate, which can translate to and from 
fifty languages. That’s 2,500 different translation directions, although for most 

language pairs, rather than translate language 1 directly into language 2, it will 
translate language 1 into English and then English into language 2. That reduces 
the number of translators Google needed to build to ninety-eight (plus a limited 
number of non-English pairs for which there is direct translation). The Google 
translators do not use grammatical rules; rather, they create vast databases for 
each language pair of common translations based on large “Rosetta stone” 
corpora of translated documents between two languages. For the six languages 
that constitute the official languages of the United Nations, Google has used 
United Nations documents, as they are published in all six languages. For less 
common languages, other sources have been used. 

The results are often impressive. DARPA runs annual competitions for the 
best automated language translation systems for different language pairs, and 
Google Translate often wins for certain pairs, outperforming systems created 
directly by human linguists. 

Over the past decade two major insights have deeply influenced the natural- 
language-understanding field. The first has to do with hierarchies. Although the 
Google approach started with association of flat word sequences from one 
language to another, the inherent hierarchical nature of language has inevitably 
crept into its operation. Systems that methodically incorporate hierarchical 
learning (such as hierarchical hidden Markov models) provided significantly 
better performance. However, such systems are not quite as automatic to build. 
Just as humans need to learn approximately one conceptual hierarchy at a time, 
the same is true for computerized systems, so the learning process needs to be 
carefully managed. 

The other insight is that hand-built rules work well for a core of common 
basic knowledge. For translations of short passages, this approach often provides 
more accurate results. For example, DARPA has rated rule-based Chinese-to- 
English translators higher than Google Translate for short passages. For what is 
called the tail of a language, which refers to the millions of infrequent phrases 
and concepts used in it, the accuracy of rule-based systems approaches an 
unacceptably low asymptote. If we plot natural-language-understanding 
accuracy against the amount of training data analyzed, rule-based systems have 
higher performance initially but level off at fairly low accuracies of about 70 
percent. In sharp contrast, statistical systems can reach the high 90s in accuracy 
but require a great deal of data to achieve that. 

Often we need a combination of at least moderate performance on a small 
amount of training data and then the opportunity to achieve high accuracies with 
a more significant quantity. Achieving moderate performance quickly enables us 
to put a system in the field and then to automatically collect training data as 

people actually use it. In this way, a great deal of learning can occur at the same 
time that the system is being used, and its accuracy will improve. The statistical 
learning needs to be fully hierarchical to reflect the nature of language, which 
also reflects how the human brain works. 

This is also how Siri and Dragon Go! work—using rules for the most 
common and reliable phenomena and then learning the “tail” of the language in 
the hands of real users. When the Cyc team realized that they had reached a 
ceiling of performance based on hand-coded rules, they too adopted this 
approach. Hand-coded rules provide two essential functions. They offer adequate 
initial accuracy, so that a trial system can be placed into widespread usage, 
where it will improve automatically. Secondly, they provide a solid basis for the 
lower levels of the conceptual hierarchy so that the automated learning can begin 
to learn higher conceptual levels. 

As mentioned above, Watson represents a particularly impressive example 
of the approach of combining hand-coded rules with hierarchical statistical 
learning. IBM combined a number of leading natural-language programs to 
create a system that could play the natural-language game of Jeopardy! On 
February 14-16, 2011, Watson competed with the two leading human players: 
Brad Rutter, who had won more money than anyone else on the quiz show, and 
Ken Jennings, who had previously held the Jeopardy! championship for the 
record time of seventy-five days. 

By way of context, I had predicted in my first book, The Age of Intelligent 
Machines, written in the mid-1980s, that a computer would take the world chess 
championship by 1998. I also predicted that when that happened, we would 
either downgrade our opinion of human intelligence, upgrade our opinion of 
machine intelligence, or downplay the importance of chess, and that if history 
was a guide, we would minimize chess. Both of these things happened in 1997. 
When IBM’s chess supercomputer Deep Blue defeated the reigning human 
world chess champion, Garry Kasparov, we were immediately treated to 
arguments that it was to be expected that a computer would win at chess because 
computers are logic machines, and chess, after all, is a game of logic. Thus Deep 
Blue’s victory was judged to be neither surprising nor significant. Many of its 
critics went on to argue that computers would never master the subtleties of 
human language, including metaphors, similes, puns, double entendres, and 

Amount of Training Data 

The accuracy of natural-language-understanding systems as a function 
of the amount of training data. The best approach is to combine rules for the 
“core” of the language and a data-based approach for the “tail” of the 

That is at least one reason why Watson represents such a significant 
milestone: Jeopardy! is precisely such a sophisticated and challenging language 
task. Typical Jeopardy! queries includes many of these vagaries of human 
language. What is perhaps not evident to many observers is that Watson not only 
had to master the language in the unexpected and convoluted queries, but for the 
most part its knowledge was not hand-coded. It obtained that knowledge by 
actually reading 200 million pages of natural-language documents, including all 
of Wikipedia and other encyclopedias, comprising 4 trillion bytes of language- 
based knowledge. As readers of this book are well aware, Wikipedia is not 
written in LISP or CycL, but rather in natural sentences that have all of the 
ambiguities and intricacies inherent in language. Watson needed to consider all 4 
trillion characters in its reference material when responding to a question. (I 
realize that Jeopardy! queries are answers in search of a question, but this is a 
technicality—they ultimately are really questions.) If Watson can understand and 
respond to questions based on 200 million pages—in three seconds!—there is 
nothing to stop similar systems from reading the other billions of documents on 
the Web. Indeed, that effort is now under way. 

When we were developing character and speech recognition systems and 
early natural-language-understanding systems in the 1970s through 1990s, we 
used a methodology of incorporating an “expert manager.” We would develop 
multiple systems to do the same thing but would incorporate somewhat different 
approaches in each one. Some of the differences were subtle, such as variations 

in the parameters controlling the mathematics of the learning algorithm. Some 
variations were fundamental, such as including rule-based systems instead of 
hierarchical statistical learning systems. The expert manager was itself a 
software program that was programmed to learn the strengths and weaknesses of 
these different systems by examining their performance in real-world situations. 
It was based on the notion that these strengths were orthogonal; that is, one 
system would tend to be strong where another was weak. Indeed, the overall 
performance of the combined systems with the trained expert manager in charge 
was far better than any of the individual systems. 

Watson works the same way. Using an architecture called UIMA 
(Unstructured Information Management Architecture), Watson deploys literally 
hundreds of different systems—many of the individual language components in 
Watson are the same ones that are used in publicly available natural-language- 
understanding systems—all of which are attempting to either directly come up 
with a response to the Jeopardy! query or else at least provide some 
disambiguation of the query. UIMA is basically acting as the expert manager to 
intelligently combine the results of the independent systems. UIMA goes 
substantially beyond earlier systems, such as the one we developed in the 
predecessor company to Nuance, in that its individual systems can contribute to 
a result without necessarily coming up with a final answer. It is sufficient if a 
subsystem helps narrow down the solution. UIMA is also able to compute how 
much confidence it has in the final answer. The human brain does this also—we 
are probably very confident of our response when asked for our mother’s first 
name, but we are less so in coming up with the name of someone we met 
casually a year ago. 

Thus rather than come up with a single elegant approach to understanding 
the language problem inherent in Jeopardy! the IBM scientists combined all of 
the state-of-the-art language-understanding modules they could get their hands 
on. Some use hierarchical hidden Markov models; some use mathematical 
variants of HHMM; others use rule-based approaches to code directly a core set 
of reliable rules. UIMA evaluates the performance of each system in actual use 
and combines them in an optimal way. There is some misunderstanding in the 
public discussions of Watson in that the IBM scientists who created it often 
focus on UIMA, which is the expert manager they created. This leads to 
comments by some observers that Watson has no real understanding of language 
because it is difficult to identify where this understanding resides. Although the 
UIMA framework also learns from its own experience, Watson’s 
“understanding” of language cannot be found in UIMA alone but rather is 
distributed across all of its many components, including the self-organizing 

language modules that use methods similar to HHMM. 

A separate part of Watson’s technology uses UIMA’s confidence estimate in 
its answers to determine how to place Jeopardy! bets. While the Watson system 
is specifically optimized to play this particular game, its core language- and 
knowledge-searching technology can easily be adapted to other broad tasks. One 
might think that less commonly shared professional knowledge, such as that in 
the medical field, would be more difficult to master than the general-purpose 
“common” knowledge that is required to play Jeopardy! Actually, the opposite is 
the case: Professional knowledge tends to be more highly organized, structured, 
and less ambiguous than its commonsense counterpart, so it is highly amenable 
to accurate natural-language understanding using these techniques. As 
mentioned, IBM is currently working with Nuance to adapt the Watson 
technology to medicine. 

The conversation that takes place when Watson is playing Jeopardy! is a 
brief one: A question is posed, and Watson comes up with an answer. (Again, 
technically, it comes up with a question to respond to an answer.) It does not 
engage in a conversation that would require tracking all of the earlier statements 
of all participants. (Siri actually does do this to a limited extent: If you ask it to 
send a message to your wife, it will ask you to identify her, but it will remember 
who she is for subsequent requests.) Tracking all of the information in a 
conversation—a task that would clearly be required to pass the Turing test—is a 
significant additional requirement but not fundamentally more difficult than 
what Watson is doing already. After all, Watson has read hundreds of millions of 
pages of material, which obviously includes many stories, so it is capable of 
tracking through complicated sequential events. It should therefore be able to 
follow its own conversations and take that into consideration in its subsequent 

Another limitation of the Jeopardy! game is that the answers are generally 
brief: It does not, for example, pose questions of the sort that ask contestants to 
name the five primary themes of A Tale of Two Cities. To the extent that it can 
find documents that do discuss the themes of this novel, a suitably modified 
version of Watson should be able to respond to this. Coming up with such 
themes on its own from just reading the book, and not essentially copying the 
thoughts (even without the words) of other thinkers, is another matter. Doing so 
would constitute a higher-level task than Watson is capable of today—it is what I 
call a Turing test-level task. (That being said, I will point out that most humans 
do not come up with their own original thoughts either but copy the ideas of 
their peers and opinion leaders.) At any rate, this is 2012, not 2029, so I would 
not expect Turing test-level intelligence yet. On yet another hand, I would point 

out that evaluating the answers to questions such as finding key ideas in a novel 
is itself not a straightforward task. If someone is asked who signed the 
Declaration of Independence, one can determine whether or not her response is 
true or false. The validity of answers to higher-level questions such as describing 
the themes of a creative work is far less easily established. 

It is noteworthy that although Watson’s language skills are actually 
somewhat below that of an educated human, it was able to defeat the best two 
Jeopardy! players in the world. It could accomplish this because it is able to 
combine its language ability and knowledge understanding with the perfect 
recall and highly accurate memories that machines possess. That is why we have 
already largely assigned our personal, social, and historical memories to them. 

Although I’m not prepared to move up my prediction of a computer passing 
the Turing test by 2029, the progress that has been achieved in systems like 
Watson should give anyone substantial confidence that the advent of Turing- 
level AI is close at hand. If one were to create a version of Watson that was 
optimized for the Turing test, it would probably come pretty close. 

American philosopher John Searle (born in 1932) argued recently that 
Watson is not capable of thinking. Citing his “Chinese room” thought 
experiment (which I will discuss further in chapter 11 ). he states that Watson is 
only manipulating symbols and does not understand the meaning of those 
symbols. Actually, Searle is not describing Watson accurately, since its 
understanding of language is based on hierarchical statistical processes—not the 
manipulation of symbols. The only way that Searle’s characterization would be 
accurate is if we considered every step in Watson’s self-organizing processes to 
be “the manipulation of symbols.” But if that were the case, then the human 
brain would not be judged capable of thinking either. 

It is amusing and ironic when observers criticize Watson for just doing 
statistical analysis of language as opposed to possessing the “true” understanding 
of language that humans have. Hierarchical statistical analysis is exactly what 
the human brain is doing when it is resolving multiple hypotheses based on 
statistical inference (and indeed at every level of the neocortical hierarchy). Both 
Watson and the human brain learn and respond based on a similar approach to 
hierarchical understanding. In many respects Watson’s knowledge is far more 
extensive than a human’s; no human can claim to have mastered all of 
Wikipedia, which is only part of Watson’s knowledge base. Conversely, a human 
can today master more conceptual levels than Watson, but that is certainly not a 
permanent gap. 

One important system that demonstrates the strength of computing applied 
to organized knowledge is Wolfram Alpha, an answer engine (as opposed to a 

search engine) developed by British mathematician and scientist Dr. Wolfram 
(born 1959) and his colleagues at Wolfram Research. For example, if you ask 
Wolfram Alpha (at, “How many primes are there under a 
million?” it will respond with “78,498.” It did not look up the answer, it 
computed it, and following the answer it provides the equations it used. If you 
attempted to get that answer using a conventional search engine, it would direct 
you to links where you could find the algorithms required. You would then have 
to plug those formulas into a system such as Mathematica, also developed by Dr. 
Wolfram, but this would obviously require a lot more work (and understanding) 
than simply asking Alpha. 

Indeed, Alpha consists of 15 million lines of Mathematica code. What 
Alpha is doing is literally computing the answer from approximately 10 trillion 
bytes of data that have been carefully curated by the Wolfram Research staff. 
You can ask a wide range of factual questions, such as “What country has the 
highest GDP per person?” (Answer: Monaco, with $212,000 per person in U.S. 
dollars), or “How old is Stephen Wolfram?” (Answer: 52 years, 9 months, 2 days 
as of the day I am writing this). As mentioned, Alpha is used as part of Apple’s 
Siri; if you ask Siri a factual question, it is handed off to Alpha to handle. Alpha 
also handles some of the searches posed to Microsoft’s Bing search engine. 

In a recent blog post, Dr. Wolfram reported that Alpha is now providing 
successful responses 90 percent of the time.— He also reports an exponential 
decrease in the failure rate, with a half-life of around eighteen months. It is an 
impressive system, and uses handcrafted methods and hand-checked data. It is a 
testament to why we created computers in the first place. As we discover and 
compile scientific and mathematical methods, computers are far better than 
unaided human intelligence in implementing them. Most of the known scientific 
methods have been encoded in Alpha, along with continually updated data on 
topics ranging from economics to physics. In a private conversation I had with 
Dr. Wolfram, he estimated that self-organizing methods such as those used in 
Watson typically achieve about an 80 percent accuracy when they are working 
well. Alpha, he pointed out, is achieving about a 90 percent accuracy. Of course, 
there is self-selection in both of these accuracy numbers in that users (such as 
myself) have learned what kinds of questions Alpha is good at, and a similar 
factor applies to the self-organizing methods. Eighty percent appears to be a 
reasonable estimate of how accurate Watson is on Jeopardy! queries, but this 
was sufficient to defeat the best humans. 

It is my view that self-organizing methods such as I articulated in the 
pattern recognition theory of mind are needed to understand the elaborate and 
often ambiguous hierarchies we encounter in real-world phenomena, including 

human language. An ideal combination for a robustly intelligent system would 
be to combine hierarchical intelligence based on the PRTM (which I contend is 
how the human brain works) with precise codification of scientific knowledge 
and data. That essentially describes a human with a computer. We will enhance 
both poles of intelligence in the years ahead. With regard to our biological 
intelligence, although our neocortex has significant plasticity, its basic 
architecture is limited by its physical constraints. Putting additional neocortex 
into our foreheads was an important evolutionary innovation, but we cannot now 
easily expand the size of our frontal lobes by a factor of a thousand, or even by 
10 percent. That is, we cannot do so biologically, but that is exactly what we will 
do technologically. 

A Strategy for Creating a Mind 

There are billions of neurons in our brains, but what are neurons? Just cells. 
The brain has no knowledge until connections are made between neurons. 
All that we know, all that we are, comes from the way our neurons are 

—Tim Berners-Lee 

Let’s use the observations I have discussed above to begin building a brain. We 
will start by building a pattern recognizer that meets the necessary attributes. 
Next we’ll make as many copies of the recognizer as we have memory and 
computational resources to support. Each recognizer computes the probability 
that its pattern has been recognized. In doing so, it takes into consideration the 
observed magnitude of each input (in some appropriate continuum) and matches 
these against the learned size and size variability parameters associated with 
each input. The recognizer triggers its simulated axon if that computed 
probability exceeds a threshold. This threshold and the parameters that control 
the computation of the pattern’s probability are among the parameters we will 
optimize with a genetic algorithm. Because it is not a requirement that every 
input be active for a pattern to be recognized, this provides for autoassociative 
recognition (that is, recognizing a pattern based on only part of the pattern being 
present). We also allow for inhibitory signals (signals that indicate that the 
pattern is less likely). 

Recognition of the pattern sends an active signal up the simulated axon of 
this pattern recognizer. This axon is in turn connected to one or more pattern 
recognizers at the next higher conceptual level. All of the pattern recognizers 
connected at the next higher conceptual level are accepting this pattern as one of 
its inputs. Each pattern recognizer also sends signals down to pattern recognizers 
at lower conceptual levels whenever most of a pattern has been recognized, 
indicating that the rest of the pattern is “expected.” Each pattern recognizer has 
one or more of these expected signal input channels. When an expected signal is 
received in this way, the threshold for recognition of this pattern recognizer is 
lowered (made easier). 

The pattern recognizers are responsible for “wiring” themselves to other 

pattern recognizers up and down the conceptual hierarchy. Note that all the 
“wires” in a software implementation operate via virtual links (which, like Web 
links, are basically memory pointers) and not actual wires. This system is 
actually much more flexible than that in the biological brain. In a human brain, 
new patterns have to be assigned to an actual physical pattern recognizer, and 
new connections have to be made with an actual axon-to-dendrite link. Usually 
this means taking an existing physical connection that is approximately what is 
needed and then growing the necessary axon and dendrite extensions to 
complete the full connection. 

Another technique used in biological mammalian brains is to start with a 
large number of possible connections and then prune the neural connections that 
are not used. If a biological neocortex reassigns cortical pattern recognizers that 
have already learned older patterns in order to learn more recent material, then 
the connections need to be physically reconfigured. Again, these tasks are much 
simpler in a software implementation. We simply assign new memory locations 
to a new pattern recognizer and use memory links for the connections. If the 
digital neocortex wishes to reassign cortical memory resources from one set of 
patterns to another, it simply returns the old pattern recognizers to memory and 
then makes the new assignment. This sort of “garbage collection” and 
reassignment of memory is a standard feature of the architecture of many 
software systems. In our digital brain we would also back up old memories 
before discarding them from the active neocortex, a precaution we can’t take in 
our biological brains. 

There are a variety of mathematical techniques that can be employed to 
implement this approach to self-organizing hierarchical pattern recognition. The 
method I would use is hierarchical hidden Markov models, for several reasons. 
From my personal perspective, I have several decades of familiarity with this 
method, having used it in the earliest speech recognition and natural-language 
systems starting in the 1980s. From the perspective of the overall field, there is 
greater experience with hidden Markov models than with any other approach for 
pattern recognition tasks. They are also extensively used in natural-language 
understanding. Many NLU systems use techniques that are at least 
mathematically similar to HHMM. 

Note that not all hidden Markov model systems are fully hierarchical. Some 
allow for just a few levels of hierarchy—for example, going from acoustic states 
to phonemes to words. To build a brain, we will want to enable our system to 
create as many new levels of hierarchy as needed. Also, most hidden Markov 
model systems are not fully self-organizing. Some have fixed connections, 
although these systems do effectively prune many of their starting connections 

by allowing them to evolve zero connection weights. Our systems from the 
1980s and 1990s automatically pruned connections with connection weights 
below a certain level and also allowed for making new connections to better 
model the training data and to learn on the fly. A key requirement, I believe, is to 
allow for the system to flexibly create its own topologies based on the patterns it 
is exposed to while learning. We can use the mathematical technique of linear 
programming to optimally assign connections to new pattern recognizers. 

Our digital brain will also accommodate substantial redundancy of each 
pattern, especially ones that occur frequently. This allows for robust recognition 
of common patterns and is also one of the key methods to achieving invariant 
recognition of different forms of a pattern. We will, however, need rules for how 
much redundancy to permit, as we don’t want to use up excessive amounts of 
memory on very common low-level patterns. 

The rules regarding redundancy, recognition thresholds, and the effect on 
the threshold of a “this pattern is expected” indication are a few examples of key 
overall parameters that affect the performance of this type of self-organizing 
system. I would initially set these parameters based on my intuition, but we 
would then optimize them using a genetic algorithm. 

A very important consideration is the education of a brain, whether a 
biological or a software one. As I discussed earlier, a hierarchical pattern 
recognition system (digital or biological) will only learn about two—preferably 
one—hierarchical levels at a time. To bootstrap the system I would start with 
previously trained hierarchical networks that have already learned their lessons 
in recognizing human speech, printed characters, and natural-language 
structures. Such a system would be capable of reading natural-language 
documents but would only be able to master approximately one conceptual level 
at a time. Previously learned levels would provide a relatively stable basis to 
learn the next level. The system can read the same documents over and over, 
gaining new conceptual levels with each subsequent reading, similar to the way 
people reread and achieve a deeper understanding of texts. Billions of pages of 
material are available on the Web. Wikipedia itself has about four million articles 
in the English version. 

I would also provide a critical thinking module, which would perform a 
continual background scan of all of the existing patterns, reviewing their 
compatibility with the other patterns (ideas) in this software neocortex. We have 
no such facility in our biological brains, which is why people can hold 
completely inconsistent thoughts with equanimity. Upon identifying an 
inconsistent idea, the digital module would begin a search for a resolution, 
including its own cortical structures as well as all of the vast literature available 

to it. A resolution might simply mean determining that one of the inconsistent 
ideas is simply incorrect (if contraindicated by a preponderance of conflicting 
data). More constructively, it would find an idea at a higher conceptual level that 
resolves the apparent contradiction by providing a perspective that explains each 
idea. The system would add this resolution as a new pattern and link to the ideas 
that initially triggered the search for the resolution. This critical thinking module 
would run as a continual background task. It would be very beneficial if human 
brains did the same thing. 

I would also provide a module that identifies open questions in every 
discipline. As another continual background task, it would search for solutions to 
them in other disparate areas of knowledge. As I noted, the knowledge in the 
neocortex consists of deeply nested patterns of patterns and is therefore entirely 
metaphorical. We can use one pattern to provide a solution or insight in an 
apparently disconnected field. 

As an example, recall the metaphor I used in chapter 4 relating the random 
movements of molecules in a gas to the random movements of evolutionary 
change. Molecules in a gas move randomly with no apparent sense of direction. 
Despite this, virtually every molecule in a gas in a beaker, given sufficient time, 
will leave the beaker. I noted that this provides a perspective on an important 
question concerning the evolution of intelligence. Like molecules in a gas, 
evolutionary changes also move every which way with no apparent direction. 
Yet we nonetheless see a movement toward greater complexity and greater 
intelligence, indeed to evolution’s supreme achievement of evolving a neocortex 
capable of hierarchical thinking. So we are able to gain an insight into how an 
apparently purposeless and directionless process can achieve an apparently 
purposeful result in one field (biological evolution) by looking at another field 

I mentioned earlier how Charles Lyell’s insight that minute changes to rock 
formations by streaming water could carve great valleys over time inspired 
Charles Darwin to make a similar observation about continual minute changes to 
the characteristics of organisms within a species. This metaphor search would be 
another continual background process. 

We should provide a means of stepping through multiple lists 
simultaneously to provide the equivalent of structured thought. A list might be 
the statement of the constraints that a solution to a problem must satisfy. Each 
step can generate a recursive search through the existing hierarchy of ideas or a 
search through available literature. The human brain appears to be able to handle 
only four simultaneous lists at a time (without the aid of tools such as 
computers), but there is no reason for an artificial neocortex to have such a 


We will also want to enhance our artificial brains with the kind of 
intelligence that computers have always excelled in, which is the ability to 
master vast databases accurately and implement known algorithms quickly and 
efficiently. Wolfram Alpha uniquely combines a great many known scientific 
methods and applies them to carefully collected data. This type of system is also 
going to continue to improve given Dr. Wolfram’s observation of an exponential 
decline in error rates. 

Finally, our new brain needs a purpose. A purpose is expressed as a series 
of goals. In the case of our biological brains, our goals are established by the 
pleasure and fear centers that we have inherited from the old brain. These 
primitive drives were initially set by biological evolution to foster the survival of 
species, but the neocortex has enabled us to sublimate them. Watson’s goal was 
to respond to Jeopardy! queries. Another simply stated goal could be to pass the 
Turing test. To do so, a digital brain would need a human narrative of its own 
fictional story so that it can pretend to be a biological human. It would also have 
to dumb itself down considerably, for any system that displayed the knowledge 
of, say, Watson would be quickly unmasked as nonbiological. 

More interestingly, we could give our new brain a more ambitious goal, 
such as contributing to a better world. A goal along these lines, of course, raises 
a lot of questions: Better for whom? Better in what way? For biological humans? 
For all conscious beings? If that is the case, who or what is conscious? 

As nonbiological brains become as capable as biological ones of effecting 
changes in the world—indeed, ultimately far more capable than unenhanced 
biological ones—we will need to consider their moral education. A good place to 
start would be with one old idea from our religious traditions: the golden rule. 


Shaped a little like a loaf of French country bread, our brain is a crowded 
chemistry lab, bustling with nonstop neural conversations. Imagine the 
brain, that shiny mound of being, that mouse-gray parliament of cells, that 
dream factory, that petit tyrant inside a ball of bone, that huddle of neurons 
calling all the plays, that little everywhere, that fickle pleasuredome, that 
wrinkled wardrobe of selves stuffed into the skull like too many clothes into 
a gym bag. 

—Diane Ackerman 

Brains exist because the distribution of resources necessary for survival and 
the hazards that threaten survival vary in space and time. 

—John M. Allman 

The modern geography of the brain has a deliciously antiquated feel to it— 
rather like a medieval map with the known world encircled by terra 
incognita where monsters roam. 

—David Bainbridge 

In mathematics you don’t understand things. You just get used to them. 

—John von Neumann 

E ver since the emergence of the computer in the middle of the twentieth 
century, there has been ongoing debate not only about the ultimate extent of its 
abilities but about whether the human brain itself could be considered a form of 
computer. As far as the latter question was concerned, the consensus has veered 
from viewing these two kinds of information-processing entities as being 

essentially the same to their being fundamentally different. So is the brain a 

When computers first became a popular topic in the 1940s, they were 
immediately regarded as thinking machines. The ENIAC, which was announced 
in 1946, was described in the press as a “giant brain.” As computers became 
commercially available in the following decade, ads routinely referred to them as 
brains capable of feats that ordinary biological brains could not match. 

o o o 

ample as a deek ccdcMiatf/r.. 

as ax electronic brain* 

A 1957 ad showing the popular conception of a computer as a giant 

Computer programs quickly enabled the machines to live up to this billing. 
The “general problem solver,” created in 1959 by Herbert A. Simon, J. C. Shaw, 
and Allen Newell at Carnegie Mellon University, was able to devise a proof to a 
theorem that mathematicians Bertrand Russell (1872-1970) and Alfred North 
Whitehead (1861-1947) had been unable to solve in their famous 1913 work 
Principia Mathematica. What became apparent in the decades that followed was 
that computers could readily significantly exceed unassisted human capability in 
such intellectual exercises as solving mathematical problems, diagnosing 
disease, and playing chess but had difficulty with controlling a robot tying 
shoelaces or with understanding the commonsense language that a five-year-old 
child could comprehend. Computers are only now starting to master these sorts 

of skills. Ironically, the evolution of computer intelligence has proceeded in the 
opposite direction of human maturation. 

The issue of whether or not the computer and the human brain are at some 
level equivalent remains controversial today. In the introduction I mentioned that 
there were millions of links for quotations on the complexity of the human brain. 
Similarly, a Google inquiry for “Quotations: the brain is not a computer” also 
returns millions of links. In my view, statements along these lines are akin to 
saying, “Applesauce is not an apple.” Technically that statement is true, but you 
can make applesauce from an apple. Perhaps more to the point, it is like saying, 
“Computers are not word processors.” It is true that a computer and a word 
processor exist at different conceptual levels, but a computer can become a word 
processor if it is running word processing software and not otherwise. Similarly, 
a computer can become a brain if it is running brain software. That is what 
researchers including myself are attempting to do. 

The question, then, is whether or not we can find an algorithm that would 
turn a computer into an entity that is equivalent to a human brain. A computer, 
after all, can run any algorithm that we might define because of its innate 
universality (subject only to its capacity). The human brain, on the other hand, is 
running a specific set of algorithms. Its methods are clever in that it allows for 
significant plasticity and the restructuring of its own connections based on its 
experience, but these functions can be emulated in software. 

The universality of computation (the concept that a general-purpose 
computer can implement any algorithm)—and the power of this idea—emerged 
at the same time as the first actual machines. There are four key concepts that 
underlie the universality and feasibility of computation and its applicability to 
our thinking. They are worth reviewing here, because the brain itself makes use 
of them. The first is the ability to communicate, remember, and compute 
information reliably. Around 1940, if you used the word “computer,” people 
assumed you were talking about an analog computer, in which numbers were 
represented by different levels of voltage, and specialized components could 
perform arithmetic functions such as addition and multiplication. A big 
limitation of analog computers, however, was that they were plagued by 
accuracy issues. Numbers could only be represented with an accuracy of about 
one part in a hundred, and as voltage levels representing them were processed by 
increasing numbers of arithmetic operators, errors would accumulate. If you 
wanted to perform more than a handful of computations, the results would 
become so inaccurate as to be meaningless. 

Anyone who can remember the days of recording music with analog tape 
machines will recall this effect. There was noticeable degradation on the first 

copy, as it was a little noisier than the original. (Remember that “noise” 
represents random inaccuracies.) A copy of the copy was noisier still, and by the 
tenth generation the copy was almost entirely noise. It was assumed that the 
same problem would plague the emerging world of digital computers. We can 
understand such concerns if we consider the communication of digital 
information through a channel. No channel is perfect and each one will have 
some inherent error rate. Suppose we have a channel that has a .9 probability of 
correctly transmitting each bit. If I send a message that is one bit long, the 
probability of accurately transmitting it through that channel will be .9. Suppose 
I send two bits? Now the accuracy is .9 2 = .81. How about if I send one byte 
(eight bits)? I have less than an even chance (.43 to be exact) of sending it 
correctly. The probability of accurately sending five bytes is about 1 percent. 

An obvious solution to circumvent this problem is to make the channel 
more accurate. Suppose the channel makes only one error in a million bits. If I 
send a file consisting of a half million bytes (about the size of a modest program 
or database), the probability of correctly transmitting it is less than 2 percent, 
despite the very high inherent accuracy of the channel. Given that a single-bit 
error can completely invalidate a computer program and other forms of digital 
data, that is not a satisfactory situation. Regardless of the accuracy of the 
channel, since the likelihood of an error in a transmission grows rapidly with the 
size of the message, this would seem to be an intractable barrier. 

Analog computers approached this problem through graceful degradation 
(meaning that users only presented problems in which they could tolerate small 
errors); however, if users of analog computers limited themselves to a 
constrained set of calculations, the computers did prove somewhat useful. 
Digital computers, on the other hand, require continual communication, not just 
from one computer to another, but within the computer itself. There is 
communication from its memory to and from the central processing unit. Within 
the central processing unit, there is communication from one register to another 
and back and forth to the arithmetic unit, and so forth. Even within the arithmetic 
unit, there is communication from one bit register to another. Communication is 
pervasive at every level. If we consider that error rates escalate rapidly with 
increased communication and that a single-bit error can destroy the integrity of a 
process, digital computation was doomed—or so it seemed at the time. 

Remarkably, that was the common view until American mathematician 
Claude Shannon (1916-2001) came along and demonstrated how we can create 
arbitrarily accurate communication using even the most unreliable 
communication channels. What Shannon stated in his landmark paper “A 
Mathematical Theory of Communication,” published in the Bell System 

Technical Journal in July and October 1948, and in particular in his noisy 
channel-coding theorem, was that if you have available a channel with any error 
rate (except for exactly 50 percent per bit, which would mean that the channel 
was just transmitting pure noise), you are able to transmit a message in which 
the error rate is as accurate as you desire. In other words, the error rate of the 
transmission can be one bit out of n bits, where n can be as large as you define. 
So, for example, in the extreme, if you have a channel that correctly transmits 
bits of information only 51 percent of the time (that is, it transmits the correct bit 
just slightly more often than the wrong bit), you can nonetheless transmit 
messages such that only one bit out of a million is incorrect, or one bit out of a 
trillion or a trillion trillion. 

How is this possible? The answer is through redundancy. That may seem 
obvious now, but it was not at the time. As a simple example, if I transmit each 
bit three times and take the majority vote, I will have substantially increased the 
reliability of the result. If that is not good enough, simply increase the 
redundancy until you get the reliability you need. Simply repeating information 
is the easiest way to achieve arbitrarily high accuracy rates from low-accuracy 
channels, but it is not the most efficient approach. Shannon’s paper, which 
established the field of information theory, presented optimal methods of error 
detection and correction codes that can achieve any target accuracy through any 
nonrandom channel. 

Older readers will recall telephone modems, which transmitted information 
through noisy analog phone lines. These lines featured audibly obvious hisses 
and pops and many other forms of distortion, but nonetheless were able to 
transmit digital data with very high accuracy rates, thanks to Shannon’s noisy 
channel theorem. The same issue and the same solution exist for digital memory. 
Ever wonder how CDs, DVDs, and program disks continue to provide reliable 
results even after the disk has been dropped on the floor and scratched? Again, 
we can thank Shannon. 

Computation consists of three elements: communication—which, as I 
mentioned, is pervasive both within and between computers—memory, and logic 
gates (which perform the arithmetic and logical functions). The accuracy of logic 
gates can also be made arbitrarily high by similarly using error detection and 
correction codes. It is due to Shannon’s theorem and theory that we can handle 
arbitrarily large and complex digital data and algorithms without the processes 
being disturbed or destroyed by errors. It is important to point out that the brain 
uses Shannon’s principle as well, although the evolution of the human brain 
clearly predates Shannon’s own! Most of the patterns or ideas (and an idea is 
also a pattern), as we have seen, are stored in the brain with a substantial amount 

of redundancy. A primary reason for the redundancy in the brain is the inherent 
unreliability of neural circuits. 

The second important idea on which the information age relies is the one I 
mentioned earlier: the universality of computation. In 1936 Alan Turing 
described his “Turing machine,” which was not an actual machine but another 
thought experiment. His theoretical computer consists of an infinitely long 
memory tape with a 1 or a 0 in each square. Input to the machine is presented on 
this tape, which the machine can read one square at a time. The machine also 
contains a table of rules—essentially a stored program—that consist of 
numbered states. Each rule specifies one action if the square currently being read 
is a 0, and a different action if the current square is a 1. Possible actions include 
writing a 0 or 1 on the tape, moving the tape one square to the right or left, or 
halting. Each state will then specify the number of the next state that the 
machine should be in. 

The input to the Turing machine is presented on the tape. The program runs, 
and when the machine halts, it has completed its algorithm, and the output of the 
process is left on the tape. Note that even though the tape is theoretically infinite 
in length, any actual program that does not get into an infinite loop will use only 
a finite portion of the tape, so if we limit ourselves to a finite tape, the machine 
will still solve a useful set of problems. 

If the Turing machine sounds simple, it is because that was its inventor’s 
objective. Turing wanted his machine to be as simple as possible (but no simpler, 
to paraphrase Einstein). Turing and Alonzo Church (1903-1995), his former 
professor, went on to develop the Church-Turing thesis, which states that if a 
problem that can be presented to a Turing machine is not solvable by it, it is also 
not solvable by any machine, following natural law. Even though the Turing 
machine has only a handful of commands and processes only one bit at a time, it 
can compute anything that any computer can compute. Another way to say this 
is that any machine that is “Turing complete” (that is, that has equivalent 
capabilities to a Turing machine) can compute any algorithm (any procedure that 
we can define). 

State transition diagram 










Infinite tape 

A block diagram of a Turing machine with a head that reads and writes 

the tape and an internal program consisting of state transitions. 

“Strong” interpretations of the Church-Turing thesis propose an essential 
equivalence between what a human can think or know and what is computable 
by a machine. The basic idea is that the human brain is likewise subject to 
natural law, and thus its information-processing ability cannot exceed that of a 
machine (and therefore of a Turing machine). 

We can properly credit Turing with establishing the theoretical foundation 
of computation with his 1936 paper, but it is important to note that he was deeply 
influenced by a lecture that Hungarian American mathematician John von 
Neumann (1903-1957) gave in Cambridge in 1935 on his stored program 
concept, a concept enshrined in the Turing machine.- In turn, von Neumann was 
influenced by Turing’s 1936 paper, which elegantly laid out the principles of 
computation, and made it required reading for his colleagues in the late 1930s 
and early 1940s. 2 

In the same paper Turing reports another unexpected discovery: that of 
unsolvable problems. These are problems that are well defined with unique 
answers that can be shown to exist, but that we can also prove can never be 
computed by any Turing machine—that is to say, by any machine, a reversal of 
what had been a nineteenth-century dogma that problems that could be defined 
would ultimately be solved. Turing showed that there are as many unsolvable 
problems as solvable ones. Austrian American mathematician and philosopher 
Kurt Godel reached a similar conclusion in his 1931 “incompleteness theorem.” 
We are thus left with the perplexing situation of being able to define a problem, 
to prove that a unique answer exists, and yet know that the answer can never be 

Turing had shown that at its essence, computation is based on a very simple 
mechanism. Because the Turing machine (and therefore any computer) is 

capable of basing its future course of action on results it has already computed, it 
is capable of making decisions and modeling arbitrarily complex hierarchies of 

In 1939 Turing designed an electronic calculator called Bombe that helped 
decode messages that had been encrypted by the Nazi Enigma coding machine. 
By 1943, an engineering team influenced by Turing completed what is arguably 
the first computer, the Colossus, that enabled the Allies to continue decoding 
messages from more sophisticated versions of Enigma. The Bombe and 
Colossus were designed for a single task and could not be reprogrammed for a 
different one. But they performed this task brilliantly and are credited with 
having enabled the Allies to overcome the three-to-one advantage that the 
German Luftwaffe enjoyed over the British Royal Air Force and win the crucial 
Battle of Britain, as well as to continue anticipating Nazi tactics throughout the 

It was on these foundations that John von Neumann created the architecture 
of the modern computer, which represents our third major idea. Called the von 
Neumann machine, it has remained the core structure of essentially every 
computer for the past sixty-seven years, from the microcontroller in your 
washing machine to the largest supercomputers. In a paper dated June 30, 1945, 
and titled “First Draft of a Report on the EDVAC,” von Neumann presented the 
ideas that have dominated computation ever since.- The von Neumann model 
includes a central processing unit, where arithmetical and logical operations are 
carried out; a memory unit, where the program and data are stored; mass storage; 
a program counter; and input/output channels. Although this paper was intended 
as an internal project document, it has become the bible for computer designers. 
You never know when a seemingly routine internal memo will end up 
revolutionizing the world. 

The Turing machine was not designed to be practical. Turing’s theorems 
were concerned not with the efficiency of solving problems but rather in 
examining the range of problems that could in theory be solved by computation. 
Von Neumann’s goal, on the other hand, was to create a feasible concept of a 
computational machine. His model replaces Turing’s one-bit computations with 
multiple-bit words (generally some multiple of eight bits). Turing’s memory tape 
is sequential, so Turing machine programs spend an inordinate amount of time 
moving the tape back and forth to store and retrieve intermediate results. In 
contrast, von Neumann’s memory is random access, so that any data item can be 
immediately retrieved. 

One of von Neumann’s key ideas is the stored program, which he had 
introduced a decade earlier: placing the program in the same type of random 

access memory as the data (and often in the same block of memory). This allows 
the computer to be reprogrammed for different tasks as well as for self¬ 
modifying code (if the program store is writable), which enables a powerful 
form of recursion. Up until that time, virtually all computers, including the 
Colossus, were built for a specific task. The stored program makes it possible for 
a computer to be truly universal, thereby fulfilling Turing’s vision of the 
universality of computation. 

Another key aspect of the von Neumann machine is that each instruction 
includes an operation code specifying the arithmetic or logical operation to be 
performed and the address of an operand from memory. 

Von Neumann’s concept of how a computer should be architected was 
introduced with his publication of the design of the ED VAC, a project he 
conducted with collaborators J. Presper Eckert and John Mauchly. The EDVAC 
itself did not actually run until 1951, by which time there were other stored- 
program computers, such as the Manchester Small-Scale Experimental Machine, 
ENIAC, EDSAC, and BINAC, all of which had been deeply influenced by von 
Neumann’s paper and involved Eckert and Mauchly as designers. Von Neumann 
was a direct contributor to the design of a number of these machines, including a 
later version of ENIAC, which supported a stored program. 

There were a few precursors to von Neumann’s architecture, although with 
one surprising exception, none are true von Neumann machines. In 1944 
Howard Aiken introduced the Mark I, which had an element of programmability 
but did not use a stored program. It read instructions from a punched paper tape 
and then executed each command immediately. It also lacked a conditional 
branch instruction. 

In 1941 German scientist Konrad Zuse (1910-1995) created the Z-3 
computer. It also read its program from a tape (in this case, coded on film) and 
also had no conditional branch instruction. Interestingly, Zuse had support from 
the German Aircraft Research Institute, which used the device to study wing 
flutter, but his proposal to the Nazi government for funding to replace his relays 
with vacuum tubes was turned down. The Nazis deemed computation as “not 
war important.” That perspective goes a long way, in my view, toward 
explaining the outcome of the war. 

There is actually one genuine forerunner to von Neumann’s concept, and it 
comes from a full century earlier! English mathematician and inventor Charles 
Babbage’s (1791-1871) Analytical Engine, which he first described in 1837, did 
incorporate von Neumann’s ideas and featured a stored program via punched 
cards borrowed from the Jacquard loom.- Its random access memory included 
1,000 words of 50 decimal digits each (the equivalent of about 21 kilobytes). 

Each instruction included an op code and an operand number, just like modern 
machine languages. It did include conditional branching and looping, so it was a 
true von Neumann machine. It was based entirely on mechanical gears and it 
appears that the Analytical Engine was beyond Babbage’s design and 
organizational skills. He built parts of it but it never ran. It is unclear whether the 
twentieth-century pioneers of the computer, including von Neumann, were aware 
of Babbage’s work. 

Babbage’s computer did result in the creation of the field of software 
programming. English writer Ada Byron (1815-1852), Countess of Lovelace 
and the only legitimate child of the poet Lord Byron, was the world’s first 
computer programmer. She wrote programs for the Analytical Engine, which she 
needed to debug in her own mind (since the computer never worked), a practice 
well known to software engineers today as “table checking.” She translated an 
article by the Italian mathematician Luigi Menabrea on the Analytical Engine 
and added extensive notes of her own, writing that “the Analytical Engine 
weaves algebraic patterns, just as the Jacquard loom weaves flowers and leaves.” 
She went on to provide perhaps the first speculations on the feasibility of 
artificial intelligence, but concluded that the Analytical Engine has “no 
pretensions whatever to originate anything.” 

Babbage’s conception is quite miraculous when you consider the era in 
which he lived and worked. However, by the mid-twentieth century, his ideas 
had been lost in the mists of time (although they were subsequently 
rediscovered). It was von Neumann who conceptualized and articulated the key 
principles of the computer as we know it today, and the world recognizes this by 
continuing to refer to the von Neumann machine as the principal model of 
computation. Keep in mind, though, that the von Neumann machine continually 
communicates data between its various units and within these units, so it could 
not be built without Shannon’s theorems and the methods he devised for 
transmitting and storing reliable digital information. 

That brings us to the fourth important idea, which is to go beyond Ada 
Byron’s conclusion that a computer could not think creatively and find the key 
algorithms employed by the brain and then use these to turn a computer into a 
brain. Alan Turing introduced this goal in his 1950 paper “Computing 
Machinery and Intelligence,” which includes his now-famous Turing test for 
ascertaining whether or not an AI has achieved a human level of intelligence. 

In 1956 von Neumann began preparing a series of lectures intended for the 
prestigious Silliman lecture series at Yale University. Due to the ravages of 
cancer, he never delivered these talks nor did he complete the manuscript from 
which they were to be given. This unfinished document nonetheless remains a 

brilliant and prophetic foreshadowing of what I regard as humanity’s most 
daunting and important project. It was published posthumously as The Computer 
and the Brain in 1958. It is fitting that the final work of one of the most brilliant 
mathematicians of the last century and one of the pioneers of the computer age 
was an examination of intelligence itself. This project was the earliest serious 
inquiry into the human brain from the perspective of a mathematician and 
computer scientist. Prior to von Neumann, the fields of computer science and 
neuroscience were two islands with no bridge between them. 

Von Neumann starts his discussion by articulating the similarities and 
differences between the computer and the human brain. Given when he wrote 
this manuscript, it is remarkably accurate. He noted that the output of neurons 
was digital—an axon either fired or it didn’t. This was far from obvious at the 
time, in that the output could have been an analog signal. The processing in the 
dendrites leading into a neuron and in the soma neuron cell body, however, was 
analog, and he described its calculations as a weighted sum of inputs with a 
threshold. This model of how neurons work led to the field of connectionism, 
which built systems based on this neuron model in both hardware and software. 
(As I described in the previous chapter , the first such connectionist system was 
created by Frank Rosenblatt as a software program on an IBM 704 computer at 
Cornell in 1957, immediately after von Neumann’s draft lectures became 
available.) We now have more sophisticated models of how neurons combine 
inputs, but the essential idea of analog processing of dendrite inputs using 
neurotransmitter concentrations has remained valid. 

Von Neumann applied the concept of the universality of computation to 
conclude that even though the architecture and building blocks appear to be 
radically different between brain and computer, we can nonetheless conclude 
that a von Neumann machine can simulate the processing in a brain. The 
converse does not hold, however, because the brain is not a von Neumann 
machine and does not have a stored program as such (albeit we can simulate a 
very simple Turing machine in our heads). Its algorithm or methods are implicit 
in its structure. Von Neumann correctly concludes that neurons can learn patterns 
from their inputs, which we have now established are coded in part in dendrite 
strengths. What was not known in von Neumann’s time is that learning also 
takes place through the creation and destruction of connections between neurons. 

Von Neumann presciently notes that the speed of neural processing is 
extremely slow, on the order of a hundred calculations per second, but that the 
brain compensates for this through massive parallel processing—another 
unobvious and key insight. Von Neumann argued that each one of the brain’s 
10 10 neurons (a tally that itself was reasonably accurate; estimates today are 

between 10 10 and 10 11 ) was processing at the same time. In fact, each of the 
connections (with an average of about 10 3 to 10 4 connections per neuron) is 
computing simultaneously. 

Von Neumann’s estimates and his descriptions of neural processing are 
remarkable, given the primitive state of neuroscience at the time. One aspect of 
his work that I do disagree with, however, is his assessment of the brain’s 
memory capacity. He assumes that the brain remembers every input for its entire 
life. Von Neumann assumes an average life span of 60 years, or about 2 x 10 9 
seconds. With about 14 inputs to each neuron per second (which is actually low 
by at least three orders of magnitude) and with 10 10 neurons, he arrives at an 
estimate of about 10 2 ° bits for the brain’s memory capacity. The reality, as I have 
noted earlier, is that we remember only a very small fraction of our thoughts and 
experiences, and even these memories are not stored as bit patterns at a low level 
(such as a video image), but rather as sequences of higher-level patterns. 

As von Neumann describes each mechanism in the brain, he shows how a 
modern computer could accomplish the same thing, despite their apparent 
differences. The brain’s analog mechanisms can be simulated through digital 
ones because digital computation can emulate analog values to any desired 
degree of precision (and the precision of analog information in the brain is quite 
low). The brain’s massive parallelism can be simulated as well, given the 
significant speed advantage of computers in serial computation (an advantage 
that has vastly expanded over time). In addition, we can also use parallel 
processing in computers by using parallel von Neumann machines—which is 
exactly how supercomputers work today. 

Von Neumann concludes that the brain’s methods cannot involve lengthy 
sequential algorithms, when one considers how quickly humans are able to make 
decisions combined with the very slow computational speed of neurons. When a 
third baseman fields a ball and decides to throw to first rather than to second 
base, he makes this decision in a fraction of a second, which is only enough time 
for each neuron to go through a handful of cycles. Von Neumann concludes 
correctly that the brain’s remarkable powers come from all its 100 billion 
neurons being able to process information simultaneously. As I have noted, the 
visual cortex makes sophisticated visual judgments in only three or four neural 

There is considerable plasticity in the brain, which enables us to learn. But 
there is far greater plasticity in a computer, which can completely restructure its 
methods by changing its software. Thus, in that respect, a computer will be able 
to emulate the brain, but the converse is not the case. 

When von Neumann compared the capacity of the brain’s massively 
parallel organization to the (few) computers of his time, it was clear that the 
brain had far greater memory and speed. By now the first supercomputer to 
achieve specifications matching some of the more conservative estimates of the 
speed required to functionally simulate the human brain (about 10 16 operations 
per second) has been built.- (I estimate that this level of computation will cost 
$1,000 by the early 2020s.) With regard to memory we are even closer. Even 
though it was remarkably early in the history of the computer when his 
manuscript was written, von Neumann nonetheless had confidence that both the 
hardware and software of human intelligence would ultimately fall into place, 
which was his motivation for having prepared these lectures. 

Von Neumann was deeply aware of the increasing pace of progress and its 
profound implications for humanity’s future. A year after his death in 1957, 
fellow mathematician Stan Ulam quoted him as having said in the early 1950s 
that “the ever accelerating progress of technology and changes in the mode of 
human life give the appearance of approaching some essential singularity in the 
history of the race beyond which human affairs, as we know them, could not 
continue.” This is the first known use of the word “singularity” in the context of 
human technological history. 

Von Neumann’s fundamental insight was that there is an essential 
equivalence between a computer and the brain. Note that the emotional 
intelligence of a biological human is part of its intelligence. If von Neumann’s 
insight is correct, and if one accepts my own leap of faith that a nonbiological 
entity that convincingly re-creates the intelligence (emotional and otherwise) of 
a biological human is conscious (see the next chapter !, then one would have to 
conclude that there is an essential equivalence between a computer—with the 
right software —and a (conscious) mind. So is von Neumann correct? 

Most computers today are entirely digital, whereas the human brain 
combines digital and analog methods. But analog methods are easily and 
routinely re-created by digital ones to any desired level of accuracy. American 
computer scientist Carver Mead (born in 1934) has shown that we can directly 
emulate the brain’s analog methods in silicon, which he has demonstrated with 
what he calls “neuromorphic” chips.- Mead has demonstrated how this approach 
can be thousands of times more efficient than digitally emulating analog 
methods. As we codify the massively repeated neocortical algorithm, it will 
make sense to use Mead’s approach. The IBM Cognitive Computing Group, led 
by Dharmendra Modha, has introduced chips that emulate neurons and their 
connections, including the ability to form new connections. 2 Called 

“SyNAPSE,” one of the chips provides a direct simulation of 256 neurons with 
about a quarter million synaptic connections. The goal of the project is to create 
a simulated neocortex with 10 billion neurons and 100 trillion connections— 
close to a human brain—that uses only one kilowatt of power. 

As von Neumann described over a half century ago, the brain is extremely 
slow but massively parallel. Today’s digital circuits are at least 10 million times 
faster than the brain’s electrochemical switches. Conversely, all 300 million of 
the brain’s neocortical pattern recognizers process simultaneously, and all 
quadrillion of its interneuronal connections are potentially computing at the 
same time. The key issue for providing the requisite hardware to successfully 
model a human brain, though, is the overall memory and computational 
throughput required. We do not need to directly copy the brain’s architecture, 
which would be a very inefficient and inflexible approach. 

Let’s estimate what those hardware requirements are. Many projects have 
attempted to emulate the type of hierarchical learning and pattern recognition 
that takes place in the neocortical hierarchy, including my own work with 
hierarchical hidden Markov models. A conservative estimate from my own 
experience is that emulating one cycle in a single pattern recognizer in the 
biological brain’s neocortex would require about 3,000 calculations. Most 
simulations run at a fraction of this estimate. With the brain mnning at about 10 2 
(100) cycles per second, that comes to 3 x 10 5 (300,000) calculations per second 
per pattern recognizer. Using my estimate of 3 x 10 8 (300 million) pattern 
recognizers, we get about 10 14 (100 trillion) calculations per second, a figure 
that is consistent with my estimate in The Singularity Is Near. In that book I 
projected that to functionally simulate the brain would require between 10 14 and 
10 16 calculations per second (cps) and used 10 16 cps to be conservative. AI 
expert Hans Moravec’s estimate, based on extrapolating the computational 
requirement of the early (initial) visual processing across the entire brain, is 10 14 
cps, which matches my own assessment here. 

Routine desktop machines can reach 10 10 cps, although this level of 
performance can be significantly amplified by using cloud resources. The fastest 
supercomputer, Japan’s K Computer, has already reached 10 16 cps.- Given that 
the algorithm of the neocortex is massively repeated, the approach of using 
neuromorphic chips such as the IBM SyNAPSE chips mentioned above is also 

In terms of memory requirement, we need about 30 bits (about four bytes) 
for one connection to address one of 300 million other pattern recognizers. If we 
estimate an average of eight inputs to each pattern recognizer, that comes to 32 

bytes per recognizer. If we add a one-byte weight for each input, that brings us to 
40 bytes. Add another 32 bytes for downward connections, and we are at 72 
bytes. Note that the branching-up-and-down figure will often be much higher 
than eight, though these very large branching trees are shared by many 
recognizers. For example, there may be hundreds of recognizers involved in 
recognizing the letter “p.” These will feed up into thousands of such recognizers 
at this next higher level that deal with words and phrases that include “p.” 
However, each “p” recognizer does not repeat the tree of connections that feeds 
up to all of the words and phrases that include “p”—they all share one such tree 
of connections. The same is true of downward connections: A recognizer that is 
responsible for the word “APPLE” will tell all of the thousands of “E” 
recognizers at a level below it that an “E” is expected if it has already seen “A,” 
“P,” “P,” and “L.” That tree of connections is not repeated for each word or 
phrase recognizer that wants to inform the next lower level that an “E” is 
expected. Again, they are shared. For this reason, an overall estimate of eight up 
and eight down on average per pattern recognizer is reasonable. Even if we 
increase this particular estimate, it does not significantly change the order of 
magnitude of the resulting estimate. 

With 3 x io 8 (300 million) pattern recognizers at 72 bytes each, we get an 
overall memory requirement of about 2 x IO 10 (20 billion) bytes. That is actually 
a quite modest number that routine computers today can exceed. 

These estimates are intended only to provide rough estimates of the order of 
magnitude required. Given that digital circuits are inherently about 10 million 
times faster than the biological neocortical circuits, we do not need to match the 
human brain for parallelism—modest parallel processing (compared with the 
trillions-fold parallelism of the human brain) will be sufficient. We can see that 
the necessary computational requirements are coming within reach. The brain’s 
rewiring of itself—dendrites are continually creating new synapses—can also be 
emulated in software using links, a far more flexible system than the brain’s 
method of plasticity, which as we have seen is impressive but limited. 

The redundancy used by the brain to achieve robust invariant results can 
certainly be replicated in software emulations. The mathematics of optimizing 
these types of self-organizing hierarchical learning systems is well understood. 
The organization of the brain is far from optimal. Of course it didn’t need to be 
—it only needed to be good enough to achieve the threshold of being able to 
create tools that would compensate for its own limitations. 

Another restriction of the human neocortex is that there is no process that 
eliminates or even reviews contradictory ideas, which accounts for why human 

thinking is often massively inconsistent. We have a weak mechanism to address 
this called critical thinking, but this skill is not practiced nearly as often as it 
should be. In a software-based neocortex, we can build in a process that reveals 
inconsistencies for further review. 

It is important to note that the design of an entire brain region is simpler 
than the design of a single neuron. As discussed earlier, models often get simpler 
at a higher level—consider an analogy with a computer. We do need to 
understand the detailed physics of semiconductors to model a transistor, and the 
equations underlying a single real transistor are complex. A digital circuit that 
multiples two numbers requires hundreds of them. Yet we can model this 
multiplication circuit very simply with one or two formulas. An entire computer 
with billions of transistors can be modeled through its instruction set and register 
description, which can be described on a handful of written pages of text and 
formulas. The software programs for an operating system, language compilers, 
and assemblers are reasonably complex, but modeling a particular program—for 
example, a speech recognition program based on hierarchical hidden Markov 
modeling—may likewise be described in only a few pages of equations. 
Nowhere in such a description would be found the details of semiconductor 
physics or even of computer architecture. 

A similar observation holds true for the brain. A particular neocortical 
pattern recognizer that detects a particular invariant visual feature (such as a 
face) or that performs a bandpass filtering (restricting input to a specific 
frequency range) on sound or that evaluates the temporal proximity of two 
events can be described with far fewer specific details than the actual physics 
and chemical relations controlling the neurotransmitters, ion channels, and other 
synaptic and dendritic variables involved in the neural processes. Although all of 
this complexity needs to be carefully considered before advancing to the next 
higher conceptual level, much of it can be simplified as the operating principles 
of the brain are revealed. 



Minds are simply what brains do. 

—Marvin Minsky, The Society of Mind 

When intelligent machines are constructed, we should not be surprised to 
find them as confused and as stubborn as men in their convictions about 
mind-matter, consciousness, free will, and the like. 

—Marvin Minsky, The Society of Mind 

Who Is Conscious? 

The real history of consciousness starts with one’s first lie. 

—Joseph Brodsky 

Suffering is the sole origin of consciousness. 

—Fyodor Dostoevsky, Notes from Underground 

There is a kind of plant that eats organic food with its flowers: when a fly 
settles upon the blossom, the petals close upon it and hold it fast till the 
plant has absorbed the insect into its system; but they will close on nothing 
but what is good to eat; of a drop of rain or a piece of stick they will take no 
notice. Curious! that so unconscious a thing should have such a keen eye to 
its own interest. If this is unconsciousness, where is the use of 

—Samuel Butler, 1871 

w e have been examining the brain as an entity that is capable of certain levels 
of accomplishment. But that perspective essentially leaves our selves out of the 
picture. We appear to live in our brains. We have subjective lives. How does the 
objective view of the brain that we have discussed up until now relate to our own 
feelings, to our sense of being the person having the experiences? 

British philosopher Colin McGinn (born in 1950) writes that discussing 
“consciousness can reduce even the most fastidious thinker to blabbering 
incoherence.” The reason for this is that people often have unexamined and 
inconsistent views on exactly what the term means. 

Many observers consider consciousness to be a form of performance—for 
example, the capacity for self-reflection, that is, the ability to understand one’s 
own thoughts and to explain them. I would describe that as the ability to think 
about one’s own thinking. Presumably, we could come up with a way of 
evaluating this ability and then use this test to separate conscious things from 
unconscious things. 

However, we quickly get into trouble in trying to implement this approach. 
Is a baby conscious? A dog? They’re not very good at describing their own 
thinking process. There are people who believe that babies and dogs are not 
conscious beings precisely because they cannot explain themselves. How about 
the computer known as Watson? It can be put into a mode where it actually does 
explain how it came up with a given answer. Because it contains a model of its 
own thinking, is Watson therefore conscious whereas the baby and the dog are 

Before we proceed to parse this question further, it is important to reflect on 
the most significant distinction relating to it: What is it that we can ascertain 
from science, versus what remains truly a matter of philosophy? One view is that 
philosophy is a kind of halfway house for questions that have not yet yielded to 
the scientific method. According to this perspective, once science advances 
sufficiently to resolve a particular set of questions, philosophers can then move 
on to other concerns, until such time that science resolves them also. This view 
is endemic where the issue of consciousness is concerned, and specifically the 
question “What and who is conscious?” 

Consider these statements by philosopher John Searle: “We know that 
brains cause consciousness with specific biological mechanisms.... The essential 
thing is to recognize that consciousness is a biological process like digestion, 
lactation, photosynthesis, or mitosis.... The brain is a machine, a biological 
machine to be sure, but a machine all the same. So the first step is to figure out 
how the brain does it and then build an artificial machine that has an equally 
effective mechanism for causing consciousness.”- People are often surprised to 
see these quotations because they assume that Searle is devoted to protecting the 
mystery of consciousness against reductionists like Ray Kurzweil. 

The Australian philosopher David Chalmers (born in 1966) has coined the 
term “the hard problem of consciousness” to describe the difficulty of pinning 
down this essentially indescribable concept. Sometimes a brief phrase 
encapsulates an entire school of thought so well that it becomes emblematic (for 
example, Hannah Arendt’s “the banality of evil”). Chalmers’s famous 
formulation accomplishes this very well. 

When discussing consciousness, it becomes very easy to slip into 
considering the observable and measurable attributes that we associate with 
being conscious, but this approach misses the very essence of the idea. I just 
mentioned the concept of metacognition—the idea of thinking about one’s own 
thinking—as one such correlate of consciousness. Other observers conflate 
emotional intelligence or moral intelligence with consciousness. But, again, our 
ability to express a loving sentiment, to get the joke, or to be sexy are simply 

types of performances—impressive and intelligent perhaps, but skills that can 
nonetheless be observed and measured (even if we argue about how to assess 
them). Figuring out how the brain accomplishes these sorts of tasks and what is 
going on in the brain when we do them constitutes Chalmers’s “easy” question 
of consciousness. Of course, the “easy” problem is anything but and represents 
perhaps the most difficult and important scientific quest of our era. Chalmers’s 
“hard” question, meanwhile, is so hard that it is essentially ineffable. 

In support of this distinction, Chalmers introduces a thought experiment 
involving what he calls zombies. A zombie is an entity that acts just like a person 
but simply does not have subjective experience—that is, a zombie is not 
conscious. Chalmers argues that since we can conceive of zombies, they are at 
least logically possible. If you were at a cocktail party and there were both 
“normal” humans and zombies, how would you tell the difference? Perhaps this 
sounds like a cocktail party you have attended. 

Many people answer this question by saying they would interrogate 
individuals they wished to assess about their emotional reactions to events and 
ideas. A zombie, they believe, would betray its lack of subjective experience 
through a deficiency in certain types of emotional responses. But an answer 
along these lines simply fails to appreciate the assumptions of the thought 
experiment. If we encountered an unemotional person (such as an individual 
with certain emotional deficits, as is common in certain types of autism) or an 
avatar or a robot that was not convincing as an emotional human being, then that 
entity is not a zombie. Remember: According to Chalmers’s assumption, a 
zombie is completely normal in his ability to respond, including the ability to 
react emotionally; he is just lacking subjective experience. The bottom line is 
that there is no way to identify a zombie, because by definition there is no 
apparent indication of his zombie nature in his behavior. So is this a distinction 
without a difference? 

Chalmers does not attempt to answer the hard question but does provide 
some possibilities. One is a form of dualism in which consciousness per se does 
not exist in the physical world but rather as a separate ontological reality. 
According to this formulation, what a person does is based on the processes in 
her brain. Because the brain is causally closed, we can fully explain a person’s 
actions, including her thoughts, through its processes. Consciousness then exists 
essentially in another realm, or at least is a property separate from the physical 
world. This explanation does not permit the mind (that is to say, the conscious 
property associated with the brain) to causally affect the brain. 

Another possibility that Chalmers entertains, which is not logically distinct 
from his notion of dualism, and is often called panprotopsychism, holds that all 

physical systems are conscious, albeit a human is more conscious than, say, a 
light switch. I would certainly agree that a human brain has more to be conscious 
about than a light switch. 

My own view, which is perhaps a subschool of panprotopsychism, is that 
consciousness is an emergent property of a complex physical system. In this 
view a dog is also conscious but somewhat less than a human. An ant has some 
level of consciousness, too, but much less that of a dog. The ant colony, on the 
other hand, could be considered to have a higher level of consciousness than the 
individual ant; it is certainly more intelligent than a lone ant. By this reckoning, 
a computer that is successfully emulating the complexity of a human brain 
would also have the same emergent consciousness as a human. 

Another way to conceptualize the concept of consciousness is as a system 
that has “qualia.” So what are qualia? One definition of the term is “conscious 
experiences.” That, however, does not take us very far. Consider this thought 
experiment: A neuroscientist is completely color-blind—not the sort of color¬ 
blind in which one mixes up certain shades of, say, green and red (as I do), but 
rather a condition in which the afflicted individual lives entirely in a black-and- 
white world. (In a more extreme version of this scenario, she has grown up in a 
black-and-white world and has never seen any colors. Bottom line, there is no 
color in her world.) However, she has extensively studied the physics of color— 
she is aware that the wavelength of red light is 700 nanometers—as well as the 
neurological processes of a person who can experience colors normally, and thus 
knows a great deal about how the brain processes color. She knows more about 
color than most people. If you wanted to help her out and explain what this 
actual experience of “red” is like, how would you do it? 

Perhaps you would read her a section from the poem “Red” by the Nigerian 
poet Oluseyi Oluseun: 

Red the colour of blood 
the symbol of life 
Red the colour of danger 
the symbol of death 

Red the colour of roses 
the symbol of beauty 
Red the colour of lovers 
the symbol of unity 

Red the colour of tomato 
the symbol of good health 
Red the colour of hot fire 
the symbol of burning desire 

That actually would give her a pretty good idea of some of the associations 
people have made with red, and may even enable her to hold her own in a 
conversation about the color. (“Yes, I love the color red, it’s so hot and fiery, so 
dangerously beautiful...”) If she wanted to, she could probably convince people 
that she had experienced red, but all the poetry in the world would not actually 
enable her to have that experience. 

Similarly, how would you explain what it feels like to dive into water to 
someone who has never touched water? We would again be forced to resort to 
poetry, but there is really no way to impart the experience itself. These 
experiences are what we refer to as qualia. 

Many of the readers of this book have experienced the color red. But how 
do I know whether your experience of red is not the same experience that I have 
when I look at blue? We both look at a red object and state assuredly that it is 
red, but that does not answer the question. I may be experiencing what you 
experience when you look at blue, but we have both learned to call red things 
red. We could start swapping poems again, but they would simply reflect the 
associations that people have made with colors; they do not speak to the actual 
nature of the qualia. Indeed, congenitally blind people have read a great deal 
about colors, as such references are replete in literature, and thus they do have 
some version of an experience of color. How does their experience of red 
compare with the experience of sighted people? This is really the same question 
as the one concerning the woman in the black-and-white world. It is remarkable 
that such common phenomena in our lives are so completely ineffable as to 
make a simple confirmation, like one that we are experiencing the same qualia, 

Another definition of qualia is the feeling of an experience. However, this 
definition is no less circular than our attempts at defining consciousness above, 
as the phrases “feeling,” “having an experience,” and “consciousness” are all 
synonyms. Consciousness and the closely related question of qualia are a 
fundamental, perhaps the ultimate, philosophical question (although the issue of 
identity may be even more important, as I will discuss in the closing section of 
this chapter). 

So with regard to consciousness, what exactly is the question again? It is 
this: Who or what is conscious? I refer to “mind” in the title of this book rather 

than “brain” because a mind is a brain that is conscious. We could also say that a 
mind has free will and identity. The assertion that these issues are philosophical 
is itself not self-evident. I maintain that these questions can never be fully 
resolved through science. In other words, there are no falsifiable experiments 
that we can contemplate that would resolve them, not without making 
philosophical assumptions. If we were building a consciousness detector, Searle 
would want it to ascertain that it was squirting biological neurotransmitters. 
American philosopher Daniel Dennett (born in 1942) would be more flexible on 
substrate, but might want to determine whether or not the system contained a 
model of itself and of its own performance. That view comes closer to my own, 
but at its core is still a philosophical assumption. 

Proposals have been regularly presented that purport to be scientific 
theories linking consciousness to some measurable physical attribute—what 
Searle refers to as the “mechanism for causing consciousness.” American 
scientist, philosopher, and anesthesiologist Stuart Hameroff (born in 1947) has 
written that “cytoskeletal filaments are the roots of consciousness.”- He is 
referring to thin threads in every cell (including neurons but not limited to them) 
called microtubules, which give each cell structural integrity and play a role in 
cell division. His books and papers on this issue contain detailed descriptions 
and equations that explain the plausibility that the microtubules play a role in 
information processing within the cell. But the connection of microtubules to 
consciousness requires a leap of faith not fundamentally different from the leap 
of faith implicit in a religious doctrine that describes a supreme being bestowing 
consciousness (sometimes referred to as a “soul”) to certain (usually human) 
entities. Some weak evidence is proffered for Hameroff’s view, specifically the 
observation that the neurological processes that could support this purported 
cellular computing are stopped during anesthesia. But this is far from compelling 
substantiation, given that lots of processes are halted during anesthesia. We 
cannot even say for certain that subjects are not conscious when anesthetized. 
All we do know is that people do not remember their experiences afterward. 
Even that is not universal, as some people do remember—accurately—their 
experience while under anesthesia, including, for example, conversations by 
their surgeons. Called anesthesia awareness, this phenomenon is estimated to 
occur about 40,000 times a year in the United States. 2 But even setting that 
aside, consciousness and memory are completely different concepts. As I have 
discussed extensively, if I think back on my moment-to-moment experiences 
over the past day, I have had a vast number of sensory impressions yet I 
remember very few of them. Was I therefore not conscious of what I was seeing 

and hearing all day? It is actually a good question, and the answer is not so clear. 

English physicist and mathematician Roger Penrose (born in 1931) took a 
different leap of faith in proposing the source of consciousness, though his also 
concerned the microtubules—specifically, their purported quantum computing 
abilities. His reasoning, although not explicitly stated, seemed to be that 
consciousness is mysterious, and a quantum event is also mysterious, so they 
must be linked in some way. 

Penrose started his analysis with Turing’s theorems on unsolvable problems 
and Godel’s related incompleteness theorem. Turing’s premise (which was 
discussed in greater detail in chapter 8 ) is that there are algorithmic problems 
that can be stated but that cannot be solved by a Turing machine. Given the 
computational universality of the Turing machine, we can conclude that these 
“unsolvable problems” cannot be solved by any machine. Godel’s 
incompleteness theorem has a similar result with regard to the ability to prove 
conjectures involving numbers. Penrose’s argument is that the human brain is 
able to solve these unsolvable problems, so is therefore capable of doing things 
that a deterministic machine such as a computer is unable to do. His motivation, 
at least in part, is to elevate human beings above machines. But his central 
premise—that humans can solve Turing’s and Godel’s insoluble problems—is 
unfortunately simply not true. 

A famous unsolvable problem called the busy beaver problem is stated as 
follows: Find the maximum number of Is that a Turing machine with a certain 
number of states can write on its tape. So to determine the busy beaver of the 
number n, we build all of the Turing machines that have n states (which will be a 
finite number if n is finite) and then determine the largest number of Is that these 
machines write on their tapes, excluding those Turing machines that get into an 
infinite loop. This is unsolvable because as we seek to simulate all of these n- 
state Turing machines, our simulator will get into an infinite loop when it 
attempts to simulate one of the Turing machines that does get into an infinite 
loop. However, it turns out that computers have nonetheless been able to 
determine the busy beaver function for certain ns. So have humans, but 
computers have solved the problem for far more ns than unassisted humans. 
Computers are generally better than humans at solving Turing’s and Godel’s 
unsolvable problems. 

Penrose linked these claimed transcendent capabilities of the human brain 
to the quantum computing that he hypothesized took place in it. According to 
Penrose, these neural quantum effects were somehow inherently not achievable 
by computers, so therefore human thinking has an inherent edge. In fact, 
common electronics uses quantum effects (transistors rely on quantum tunneling 

of electrons across barriers); quantum computing in the brain has never been 
demonstrated; human mental performance can be satisfactorily explained by 
classical computing methods; and in any event nothing bars us from applying 
quantum computing in computers. None of these objections has ever been 
addressed by Penrose. It was when critics pointed out that the brain is a warm 
and messy place for quantum computing that Hameroff and Penrose joined 
forces. Penrose found a perfect vehicle within neurons that could conceivably 
support quantum computing—namely, the microtubules that Hameroff had 
speculated were part of the information processing within a neuron. So the 
Hameroff-Penrose thesis is that the microtubules in the neurons are doing 
quantum computing and that this is responsible for consciousness. 

This thesis has also been criticized, for example, by Swedish American 
physicist and cosmologist Max Tegmark (born in 1967), who determined that 
quantum events in microtubules could survive for only 10“ 13 seconds, which is 
much too brief a period of time either to compute results of any significance or 
to affect neural processes. There are certain types of problems for which 
quantum computing would show superior capabilities to classical computing— 
for example, the cracking of encryption codes through the factoring of large 
numbers. However, unassisted human thinking has proven to be terrible at 
solving them, and cannot match even classical computers in this area, which 
suggests that the brain is not demonstrating any quantum computing capabilities. 
Moreover, even if such a phenomenon as quantum computing in the brain did 
exist, it would not necessarily be linked to consciousness. 

You Gotta Have Faith 

What a piece of work is a man! How noble in reason! How infinite in 
faculties! In form and moving, how express and admirable! In action how 
like an angel! In apprehension, how like a god! The beauty of the world! 
The paragon of animals! And yet, to me, what is this quintessence of dust? 

—Hamlet, in Shakespeare’s Hamlet 

The reality is that these theories are all leaps of faith, and I would add that where 
consciousness is concerned, the guiding principle is “you gotta have faith”—that 
is, we each need a leap of faith as to what and who is conscious, and who and 
what we are as conscious beings. Otherwise we could not get up in the morning. 
But we should be honest about the fundamental need for a leap of faith in this 
matter and self-reflective as to what our own particular leap involves. 

People have very different leaps, despite impressions to the contrary. 
Individual philosophical assumptions about the nature and source of 
consciousness underlie disagreements on issues ranging from animal rights to 
abortion, and will result in even more contentious future conflicts over machine 
rights. My objective prediction is that machines in the future will appear to be 
conscious and that they will be convincing to biological people when they speak 
of their qualia. They will exhibit the full range of subtle, familiar emotional 
cues; they will make us laugh and cry; and they will get mad at us if we say that 
we don’t believe that they are conscious. (They will be very smart, so we won’t 
want that to happen.) We will come to accept that they are conscious persons. 
My own leap of faith is this: Once machines do succeed in being convincing 
when they speak of their qualia and conscious experiences, they will indeed 
constitute conscious persons. I have come to my position via this thought 
experiment: Imagine that you meet an entity in the future (a robot or an avatar) 
that is completely convincing in her emotional reactions. She laughs 
convincingly at your jokes, and in turn makes you laugh and cry (but not just by 
pinching you). She convinces you of her sincerity when she speaks of her fears 
and longings. In every way, she seems conscious. She seems, in fact, like a 
person. Would you accept her as a conscious person? 

If your initial reaction is that you would likely detect some way in which 

she betrays her nonbiological nature, then you are not keeping to the 
assumptions in this hypothetical situation, which established that she is fully 
convincing. Given that assumption, if she were threatened with destruction and 
responded, as a human would, with terror, would you react in the same 
empathetic way that you would if you witnessed such a scene involving a 
human? For myself, the answer is yes, and I believe the answer would be the 
same for most if not virtually all other people regardless of what they might 
assert now in a philosophical debate. Again, the emphasis here is on the word 

There is certainly disagreement on when or even whether we will encounter 
such a nonbiological entity. My own consistent prediction is that this will first 
take place in 2029 and become routine in the 2030s. But putting the time frame 
aside, I believe that we will eventually come to regard such entities as conscious. 
Consider how we already treat them when we are exposed to them as characters 
in stories and movies: R2D2 from the Star Wars movies, David and Teddy from 
the movie A.I., Data from the TV series Star Trek: The Next Generation, Johnny 
5 from the movie Short Circuit, WALL-E from Disney’s movie Wall-E, T-800— 
the (good) Terminator—in the second and later Terminator movies, Rachael the 
Replicant from the movie Blade Runner (who, by the way, is not aware that she 
is not human), Bumblebee from the movie, TV, and comic series Transformers, 
and Sonny from the movie I, Robot. We do empathize with these characters even 
though we know that they are nonbiological. We regard them as conscious 
persons, just as we do biological human characters. We share their feelings and 
fear for them when they get into trouble. If that is how we treat fictional 
nonbiological characters today, then that is how we will treat real-life 
intelligences in the future that don’t happen to have a biological substrate. 

If you do accept the leap of faith that a nonbiological entity that is 
convincing in its reactions to qualia is actually conscious, then consider what 
that implies: namely that consciousness is an emergent property of the overall 
pattern of an entity, not the substrate it runs on. 

There is a conceptual gap between science, which stands for objective 
measurement and the conclusions we can draw thereby, and consciousness, 
which is a synonym for subjective experience. We obviously cannot simply ask 
an entity in question, “Are you conscious?” If we look inside its “head,” 
biological or otherwise, to ascertain that, then we would have to make 
philosophical assumptions in determining what it is that we are looking for. The 
question as to whether or not an entity is conscious is therefore not a scientific 
one. Based on this, some observers go on to question whether consciousness 
itself has any basis in reality. English writer and philosopher Susan Blackmore 

(bom in 1951) speaks of the “grand illusion of consciousness.” She 
acknowledges the reality of the meme (idea) of consciousness—in other words, 
consciousness certainly exists as an idea, and there are a great many neocortical 
structures that deal with the idea, not to mention words that have been spoken 
and written about it. But it is not clear that it refers to something real. Blackburn 
goes on to explain that she is not necessarily denying the reality of 
consciousness, but rather attempting to articulate the sorts of dilemmas we 
encounter when we try to pin down the concept. As British psychologist and 
writer Stuart Sutherland (1927-1998) wrote in the International Dictionary of 
Psychology, “Consciousness is a fascinating but elusive phenomenon; it is 
impossible to specify what it is, what it does, or why it evolved.”- 

However, we would be well advised not to dismiss the concept too easily as 
just a polite debate between philosophers—which, incidentally, dates back two 
thousand years to the Platonic dialogues. The idea of consciousness underlies 
our moral system, and our legal system in turn is loosely built on those moral 
beliefs. If a person extinguishes someone’s consciousness, as in the act of 
murder, we consider that to be immoral, and with some exceptions, a high crime. 
Those exceptions are also relevant to consciousness, in that we might authorize 
police or military forces to kill certain conscious people to protect a greater 
number of other conscious people. We can debate the merits of particular 
exceptions, but the underlying principle holds true. 

Assaulting someone and causing her to experience suffering is also 
generally considered immoral and illegal. If I destroy my property, it is probably 
acceptable. If I destroy your property without your permission, it is probably not 
acceptable, but not because I am causing suffering to your property, but rather to 
you as the owner of the property. On the other hand, if my property includes a 
conscious being such as an animal, then I as the owner of that animal do not 
necessarily have free moral or legal rein to do with it as I wish—there are, for 
example, laws against animal cruelty. 

Because a great deal of our moral and legal system is based on protecting 
the existence of and preventing the unnecessary suffering of conscious entities, 
in order to make responsible judgments we need to answer the question as to 
who is conscious. That question is therefore not simply a matter for intellectual 
debate, as is evident in the controversy surrounding an issue like abortion. I 
should point out that the abortion issue can go somewhat beyond the issue of 
consciousness, as pro-life proponents argue that the potential for an embryo to 
ultimately become a conscious person is sufficient reason for it to be awarded 
protection, just as someone in a coma deserves that right. But fundamentally the 
issue is a debate about when a fetus becomes conscious. 

Perceptions of consciousness also often affect our judgments in 
controversial areas. Looking at the abortion issue again, many people make a 
distinction between a measure like the morning-after pill, which prevents the 
implantation of an embryo in the uterus in the first days of pregnancy, and a late- 
stage abortion. The difference has to do with the likelihood that the late-stage 
fetus is conscious. It is difficult to maintain that a few-days-old embryo is 
conscious unless one takes a panprotopsychist position, but even in these terms it 
would rank below the simplest animal in terms of consciousness. Similarly, we 
have very different reactions to the maltreatment of great apes versus, say, 
insects. No one worries too much today about causing pain and suffering to our 
computer software (although we do comment extensively on the ability of 
software to cause us suffering), but when future software has the intellectual, 
emotional, and moral intelligence of biological humans, this will become a 
genuine concern. 

Thus my position is that I will accept nonbiological entities that are fully 
convincing in their emotional reactions to be conscious persons, and my 
prediction is that the consensus in society will accept them as well. Note that this 
definition extends beyond entities that can pass the Turing test, which requires 
mastery of human language. The latter are sufficiently humanlike that I would 
include them, and I believe that most of society will as well, but I also include 
entities that evidence humanlike emotional reactions but may not be able to pass 
the Turing test—for example, young children. 

Does this resolve the philosophical question of who is conscious, at least 
for myself and others who accept this particular leap of faith? The answer is: not 
quite. We’ve only covered one case, which is that of entities that act in a 
humanlike way. Even though we are discussing future entities that are not 
biological, we are talking about entities that demonstrate convincing humanlike 
reactions, so this position is still human-centric. But what about more alien 
forms of intelligence that are not humanlike? We can imagine intelligences that 
are as complex as or perhaps vastly more complex and intricate than human 
brains, but that have completely different emotions and motivations. How do we 
decide whether or not they are conscious? 

We can start by considering creatures in the biological world that have 
brains comparable to those of humans yet evince very different sorts of 
behaviors. British philosopher David Cockburn (born in 1949) writes about 
viewing a video of a giant squid that was under attack (or at least it thought it 
was—Cockburn hypothesized that it might have been afraid of the human with 
the video camera). The squid shuddered and cowered, and Cockburn writes, “It 
responded in a way which struck me immediately and powerfully as one of fear. 

Part of what was striking in this sequence was the way in which it was possible 
to see in the behavior of a creature physically so very different from human 
beings an emotion which was so unambiguously and specifically one of fear.”- 
He concludes that the animal was feeling that emotion and he articulates the 
belief that most other people viewing that film would come to the same 
conclusion. If we accept Cockburn’s description and conclusion, then we would 
have to add giant squids to our list of conscious entities. However, this has not 
gotten us very far either, because it is still based on our empathetic reaction to an 
emotion that we recognize in ourselves. It is still a self-centric or human-centric 

If we step outside biology, nonbiological intelligence will be even more 
varied than intelligence in the biological world. For example, some entities may 
not have a fear of their own destruction, and may not have a need for the 
emotions we see in humans or in any biological creature. Perhaps they could still 
pass the Turing test, or perhaps they wouldn’t even be willing to try. 

We do in fact build robots today that do not have a sense of self- 
preservation to carry out missions in dangerous environments. They’re not 
sufficiently intelligent or complex yet for us to seriously consider their sentience, 
but we can imagine future robots of this sort that are as complex as humans. 
What about them? 

Personally I would say that if I saw in such a device’s behavior a 
commitment to a complex and worthy goal and the ability to execute notable 
decisions and actions to carry out its mission, I would be impressed and probably 
become upset if it got destroyed. This is now perhaps stretching the concept a 
bit, in that I am responding to behavior that does not include many emotions we 
consider universal in people and even in biological creatures of all kinds. But 
again, I am seeking to connect with attributes that I can relate to in myself and 
other people. The idea of an entity totally dedicated to a noble goal and carrying 
it out or at least attempting to do so without regard for its own well-being is, 
after all, not completely foreign to human experience. In this instance we are 
also considering an entity that is seeking to protect biological humans or in some 
way advance our agenda. 

What if this entity has its own goals distinct from a human one and is not 
carrying out a mission we would recognize as noble in our own terms? I might 
then attempt to see if I could connect or appreciate some of its abilities in some 
other way. If it is indeed very intelligent, it is likely to be good at math, so 
perhaps I could have a conversation with it on that topic. Maybe it would 
appreciate math jokes. 

But if the entity has no interest in communicating with me, and I don’t have 

sufficient access to its actions and decision making to be moved by the beauty of 
its internal processes, does that mean that it is not conscious? I need to conclude 
that entities that do not succeed in convincing me of their emotional reactions, or 
that don’t care to try, are not necessarily not conscious. It would be difficult to 
recognize another conscious entity without establishing some level of empathetic 
communication, but that judgment reflects my own limitations more than it does 
the entity under consideration. We thus need to proceed with humility. It is 
challenging enough to put ourselves in the subjective shoes of another human, so 
the task will be that much harder with intelligences that are extremely different 
from our own. 

What Are We Conscious Of? 

If we could look through the skull into the brain of a consciously thinking 
person, and if the place of optimal excitability were luminous, then we 
should see playing over the cerebral surface, a bright spot with fantastic, 
waving borders constantly fluctuating in size and form, surrounded by a 
darkness more or less deep, covering the rest of the hemisphere. 

—Ivan Petrovich Pavlov, 1913- 

Returning to the giant squid, we can recognize some of its apparent emotions, 
but much of its behavior is a mystery. What is it like being a giant squid? How 
does it feel as it squeezes its spineless body through a tiny opening? We don’t 
even have the vocabulary to answer this question, given that we cannot even 
describe experiences that we do share with other people, such as seeing the color 
red or feeling water splash on our bodies. 

But we don’t have to go as far as the bottom of the ocean to find mysteries 
in the nature of conscious experiences—we need only consider our own. I know, 
for example, that I am conscious. I assume that you, the reader, are conscious 
also. (As for people who have not bought my book, I am not so sure.) But what 
am I conscious of? You might ask yourself the same question. 

Try this thought experiment (which will work for those of you who drive a 
car): Imagine that you are driving in the left lane of a highway. Now close your 
eyes, grab an imagined steering wheel, and make the movements to change lanes 
to the lane to your right. 

Okay, before continuing to read, try it. 

Here is what you probably did: You held the steering wheel. You checked 
that the right lane is clear. Assuming the lane was clear, you turned the steering 
wheel to the right for a brief period. Then you straightened it out again. Job 

It’s a good thing you weren’t in a real car, because you just zoomed across 
all the lanes of the highway and crashed into a tree. While I probably should 
have mentioned that you shouldn’t try this in a real moving car (but then I 
assume you have already mastered the rule that you shouldn’t drive with your 
eyes closed), that’s not really the key problem here. If you used the procedure I 

just described—and almost everyone does when doing this thought experiment 
—you got it wrong. Turning the wheel to the right and then straightening it out 
causes the car to head in a direction that is diagonal to its original direction. It 
will cross the lane to the right, as you intended, but it will keep going to the right 
indefinitely until it zooms off the road. What you needed to do as your car 
crossed the lane to the right was to then turn the wheel to the left, just as far as 
you had turned it to the right, and then straighten it out again. This will cause the 
car to again head straight in the new lane. 

Consider the fact that if you’re a regular driver, you’ve done this maneuver 
thousands of times. Are you not conscious when you do this? Have you never 
paid attention to what you are actually doing when you change lanes? Assuming 
that you are not reading this book in a hospital while recovering from a lane¬ 
changing accident, you have clearly mastered this skill. Yet you are not 
conscious of what you did, however many times you’ve accomplished this task. 

When people tell stories of their experiences, they describe them as 
sequences of situations and decisions. But this is not how we experience a story 
in the first place. Our original experience is as a sequence of high-level patterns, 
some of which may have triggered feelings. We remember only a small subset of 
those patterns, if that. Even if we are reasonably accurate in our recounting of a 
story, we use our powers of confabulation to fill in missing details and convert 
the sequence into a coherent tale. We cannot be certain what our original 
conscious experience was from our recollection of it, yet memory is the only 
access we have to that experience. The present moment is, well, fleeting, and is 
quickly turned into a memory, or, more often, not. Even if an experience is 
turned into a memory, it is stored, as the PRTM indicates, as a high-level pattern 
composed of other patterns in a huge hierarchy. As I have pointed out several 
times, almost ah of the experiences we have (like any of the times we changed 
lanes) are immediately forgotten. So ascertaining what constitutes our own 
conscious experience is actually not attainable. 

East Is East and West Is West 

Before brains there was no color or sound in the universe, nor was there any 
flavor or aroma and probably little sense and no feeling or emotion. 

—Roger W. Sperry 

Rene Descartes walks into a restaurant and sits down for dinner. The waiter 
comes over and asks if he’d like an appetizer. 

“No thank you,” says Descartes, “I’d just like to order dinner.” 

“Would you like to hear our daily specials?” asks the waiter. 

“No,” says Descartes, getting impatient. 

“Would you like a drink before dinner?” the waiter asks. 

Descartes is insulted, since he’s a teetotaler. “I think not!” he says 
indignantly, and POOF! he disappears. 

—A joke as recalled by David Chalmers 

There are two ways to view the questions we have been considering—converse 
Western and Eastern perspectives on the nature of consciousness and of reality. 
In the Western perspective, we start with a physical world that evolves patterns 
of information. After a few billion years of evolution, the entities in that world 
have evolved sufficiently to become conscious beings. In the Eastern view, 
consciousness is the fundamental reality; the physical world only comes into 
existence through the thoughts of conscious beings. The physical world, in other 
words, is the thoughts of conscious beings made manifest. These are of course 
simplifications of complex and diverse philosophies, but they represent the 
principal polarities in the philosophies of consciousness and its relationship to 
the physical world. 

The East-West divide on the issue of consciousness has also found 
expression in opposing schools of thought in the field of subatomic physics. In 
quantum mechanics, particles exist as what are called probability fields. Any 
measurement carried out on them by a measuring device causes what is called a 
collapse of the wave function, meaning that the particle suddenly assumes a 
particular location. A popular view is that such a measurement constitutes 
observation by a conscious observer, because otherwise measurement would be a 

meaningless concept. Thus the particle assumes a particular location (as well as 
other properties, such as velocity) only when it is observed. Basically particles 
figure that if no one is bothering to look at them, they don’t need to decide where 
they are. I call this the Buddhist school of quantum mechanics, because in it 
particles essentially don’t exist until they are observed by a conscious person. 

There is another interpretation of quantum mechanics that avoids such 
anthropomorphic terminology. In this analysis, the field representing a particle is 
not a probability field, but rather just a function that has different values in 
different locations. The field, therefore, is fundamentally what the particle is. 
There are constraints on what the values of the field can be in different locations, 
because the entire field representing a particle represents only a limited amount 
of information. That is where the word “quantum” comes from. The so-called 
collapse of the wave function, this view holds, is not a collapse at all. The wave 
function actually never goes away. It is just that a measurement device is also 
made up of particles with fields, and the interaction of the particle field being 
measured and the particle fields of the measuring device results in a reading of 
the particle being in a particular location. The field, however, is still present. 
This is the Western interpretation of quantum mechanics, although it is 
interesting to note that the more popular view among physicists worldwide is 
what I have called the Eastern interpretation. 

There was one philosopher whose work spanned this East-West divide. The 
Austrian British thinker Ludwig Wittgenstein (1889-1951) studied the 
philosophy of language and knowledge and contemplated the question of what it 
is that we can really know. He pondered this subject while a soldier in World 
War I and took notes for what would be his only book published while he was 
alive, Tractatus Logico-Philosophicus. The work had an unusual structure, and it 
was only through the efforts of his former instructor, British mathematician and 
philosopher Bertrand Russell, that it found a publisher in 1921. It became the 
bible for a major school of philosophy known as logical positivism, which 
sought to define the limits of science. The book and the movement surrounding 
it were influential on Turing and the emergence of the theory of computation and 

Tractatus Logico-Philosophicus anticipates the insight that all knowledge is 
inherently hierarchical. The book itself is arranged in nested and numbered 
statements. For example, the first four statements in the book are: 

1 The world is all that is the case. 

1.1 The world is the totality of facts, not of things. 

1.11 The world is determined by the facts, and by their being all the 


1.12 For the totality of facts determines what is the case, and also 
whatever is not the case. 

Another significant statement in the Tractatus —and one that Turing would echo 
—is this: 

4.0031 All philosophy is a critique of language. 

Essentially both Tractatus Logico-Philosophicus and the logical positivism 
movement assert that physical reality exists separate from our perception of it, 
but that all we can know of that reality is what we perceive with our senses— 
which can be heightened through our tools—and the logical inferences we can 
make from these sensory impressions. Essentially Wittgenstein is attempting to 
describe the methods and goals of science. The final statement in the book is 
number 7, “What we cannot speak about we must pass over in silence.” The 
early Wittgenstein, accordingly, considers the discussion of consciousness as 
circular and tautological and therefore a waste of time. 

The later Wittgenstein, however, completely rejected this approach and 
spent all of his philosophical attention talking about matters that he had earlier 
argued should be passed over in silence. His writings on this revised thinking 
were collected and published in 1953, two years after his death, in a book called 
Philosophical Investigations. He criticized his earlier ideas in the Tractatus, 
judging them to be circular and void of meaning, and came to the view that what 
he had advised that we not speak about was in fact all that was worth reflecting 
on. These writings heavily influenced the existentialists, making Wittgenstein 
the only figure in modern philosophy to be a major architect of two leading and 
contradictory schools of thought in philosophy. 

What is it that the later Wittgenstein thought was worth thinking and talking 
about? It was issues such as beauty and love, which he recognized exist 
imperfectly as ideas in the minds of men. However, he writes that such concepts 
do exist in a perfect and idealized realm, similar to the perfect “forms” that Plato 
wrote about in the Platonic dialogues, another work that illuminated apparently 
contradictory approaches to the nature of reality. 

One thinker whose position I believe is mischaracterized is the French 
philosopher and mathematician Rene Descartes. His famous “I think, therefore I 
am” is generally interpreted to extol rational thought, in the sense that “I think, 
that is I can perform logical thought, therefore I am worthwhile.” Descartes is 
therefore considered the architect of the Western rational perspective. 

Reading this statement in the context of his other writings, however, I get a 
different impression. Descartes was troubled by what is referred to as the “mind- 
body problem”: Namely, how does a conscious mind arise from the physical 
matter of the brain? From this perspective, it seems he was attempting to push 
rational skepticism to the breaking point, so in my view what his statement really 
means is, “I think, that is to say, a subjective experience is occurring, so 
therefore all we know for sure is that something—call it I —exists.” He could not 
be certain that the physical world exists, because all we have are our own 
individual sense impressions of it, which might be wrong or completely illusory. 
We do know, however, that the experiencer exists. 

My religious upbringing was in a Unitarian church, where we studied all of 
the world’s religions. We would spend six months on, say, Buddhism and would 
go to Buddhist services, read their books, and have discussion groups with their 
leaders. Then we would switch to another religion, such as Judaism. The 
overriding theme was “many paths to the truth,” along with tolerance and 
transcendence. This last idea meant that resolving apparent contradictions 
between traditions does not require deciding that one is right and the other is 
wrong. The truth can be discovered only by finding an explanation that overrides 
—transcends—seeming differences, especially for fundamental questions of 
meaning and purpose. 

This is how I resolve the Western-Eastern divide on consciousness and the 
physical world. In my view, both perspectives have to be true. 

On the one hand, it is foolish to deny the physical world. Even if we do live 
in a simulation, as speculated by Swedish philosopher Nick Bostrom, reality is 
nonetheless a conceptual level that is real for us. If we accept the existence of the 
physical world and the evolution that has taken place in it, then we can see that 
conscious entities have evolved from it. 

On the other hand, the Eastern perspective—that consciousness is 
fundamental and represents the only reality that is truly important—is also 
difficult to deny. Just consider the precious regard we give to conscious persons 
versus unconscious things. We consider the latter to have no intrinsic value 
except to the extent that they can influence the subjective experience of 
conscious persons. Even if we regard consciousness as an emergent property of a 
complex system, we cannot take the position that it is just another attribute 
(along with “digestion” and “lactation,” to quote John Searle). It represents what 
is truly important. 

The word “spiritual” is often used to denote the things that are of ultimate 
significance. Many people don’t like to use such terminology from spiritual or 
religious traditions, because it implies sets of beliefs that they may not subscribe 

to. But if we strip away the mystical complexities of religious traditions and 
simply respect “spiritual” as implying something of profound meaning to 
humans, then the concept of consciousness fits the bill. It reflects the ultimate 
spiritual value. Indeed, “spirit” itself is often used to denote consciousness. 

Evolution can then be viewed as a spiritual process in that it creates 
spiritual beings, that is, entities that are conscious. Evolution also moves toward 
greater complexity, greater knowledge, greater intelligence, greater beauty, 
greater creativity, and the ability to express more transcendent emotions, such as 
love. These are all descriptions that people have used for the concept of God, 
albeit God is described as having no limitations in these regards. 

People often feel threatened by discussions that imply the possibility that a 
machine could be conscious, as they view considerations along these lines as a 
denigration of the spiritual value of conscious persons. But this reaction reflects 
a misunderstanding of the concept of a machine. Such critics are addressing the 
issue based on the machines they know today, and as impressive as they are 
becoming, I agree that contemporary examples of technology are not yet worthy 
of our respect as conscious beings. My prediction is that they will become 
indistinguishable from biological humans, whom we do regard as conscious 
beings, and will therefore share in the spiritual value we ascribe to 
consciousness. This is not a disparagement of people; rather, it is an elevation of 
our understanding of (some) future machines. We should probably adopt a 
different terminology for these entities, as they will be a different sort of 

Indeed, as we now look inside the brain and decode its mechanisms we 
discover methods and algorithms that we can not only understand but re-create 
—“the parts of a mill pushing on each other,” to paraphrase German 
mathematician and philosopher Gottfried Wilhelm Leibniz (1646-1716) when he 
wrote about the brain. Humans already constitute spiritual machines. Moreover, 
we will merge with the tools we are creating so closely that the distinction 
between human and machine will blur until the difference disappears. That 
process is already well under way, even if most of the machines that extend us 
are not yet inside our bodies and brains. 

Free Will 

A central aspect of consciousness is the ability to look ahead, the capability 
we call “foresight.” It is the ability to plan, and in social terms to outline a 
scenario of what is likely going to happen, or what might happen, in social 
interactions that have not yet taken place.... It is a system whereby we 
improve our chances of doing those things that will represent our own best 
interests.... I suggest that “free will” is our apparent ability to choose and 
act upon whichever of those seem most useful or appropriate, and our 
insistence upon the idea that such choices are our own. 

—Richard D. Alexander 

Shall we say that the plant does not know what it is doing merely because it 
has no eyes, or ears, or brains? If we say that it acts mechanically, and 
mechanically only, shall we not be forced to admit that sundry other and 
apparently very deliberate actions are also mechanical? If it seems to us that 
the plant kills and eats a fly mechanically, may it not seem to the plant that 
a man must kill and eat a sheep mechanically? 

—Samuel Butler, 1871 

Is the brain, which is notably double in structure, a double organ, “seeming 
parted, but yet a union in partition”? 

—Henry Maudsley- 

Redundancy, as we have learned, is a key strategy deployed by the neocortex. 
But there is another level of redundancy in the brain, in that its left and right 
hemispheres, while not identical, are largely the same. Just as certain regions of 
the neocortex normally end up processing certain types of information, the 
hemispheres also specialize to some extent—for example, the left hemisphere 
typically is responsible for verbal language. But these assignments can also be 
rerouted, to the point that we can survive and function somewhat normally with 
only one half. American neuropsychology researchers Stella de Bode and Susan 
Curtiss reported on forty-nine children who had undergone a hemispherectomy 

(removal of half of their brain), an extreme operation that is performed on 
patients with a life-threatening seizure disorder that exists in only one 
hemisphere. Some who undergo the procedure are left with deficits, but those 
deficits are specific and the patients have reasonably normal personalities. Many 
of them thrive, and it is not apparent to observers that they only have half a 
brain. De Bode and Curtiss write about left-hemispherectomized children who 
“develop remarkably good language despite removal of the ‘language’ 
hemisphere.”- They describe one such student who completed college, attended 
graduate school, and scored above average on IQ tests. Studies have shown 
minimal long-term effects on overall cognition, memory, personality, and sense 
of humor.— In a 2007 study American researchers Shearwood McClelland and 
Robert Maxwell showed similar long-term positive results in adults.— 

A ten-year-old German girl who was born with only half of her brain has 
also been reported to be quite normal. She even has almost perfect vision in one 
eye, whereas hemispherectomy patients lose part of their field of vision right 
after the operation.— Scottish researcher Lars Muckli commented, “The brain 
has amazing plasticity but we were quite astonished to see just how well the 
single hemisphere of the brain in this girl has adapted to compensate for the 
missing half.” 

While these observations certainly support the idea of plasticity in the 
neocortex, their more interesting implication is that we each appear to have two 
brains, not one, and we can do pretty well with either. If we lose one, we do lose 
the cortical patterns that are uniquely stored there, but each brain is in itself 
fairly complete. So does each hemisphere have its own consciousness? There is 
an argument to be made that such is the case. 

Consider split-brain patients, who still have both of their brain hemispheres, 
but the channel between them has been cut. The corpus callosum is a bundle of 
about 250 million axons that connects the left and right cerebral hemispheres and 
enables them to communicate and coordinate with each other. Just as two people 
can communicate closely with each other and act as a single decision maker 
while remaining separate and whole individuals, the two brain hemispheres can 
function as a unit while remaining independent. 

As the term implies, in split-brain patients the corpus callosum has been cut 
or damaged, leaving them effectively with two functional brains without a direct 
communication link between them. American psychology researcher Michael 
Gazzaniga (born in 1939) has conducted extensive experiments on what each 
hemisphere in split-brain patients is thinking. 

The left hemisphere in a split-brain patient usually sees the right visual 

field, and vice versa. Gazzaniga and his colleagues showed a split-brain patient a 
picture of a chicken claw to the right visual field (which was seen by his left 
hemisphere) and a snowy scene to the left visual field (which was seen by his 
right hemisphere). He then showed a collection of pictures so that both 
hemispheres could see them. He asked the patient to choose one of the pictures 
that went well with the first picture. The patient’s left hand (controlled by his 
right hemisphere) pointed to a picture of a shovel, whereas his right hand pointed 
to a picture of a chicken. So far so good—the two hemispheres were acting 
independently and sensibly. “Why did you choose that?” Gazzaniga asked the 
patient, who answered verbally (controlled by his left-hemisphere speech 
center), “The chicken claw obviously goes with the chicken.” But then the 
patient looked down and, noticing his left hand pointing to the shovel, 
immediately explained this (again with his left-hemisphere-controlled speech 
center) as “and you need a shovel to clean out the chicken shed.” 

This is a confabulation. The right hemisphere (which controls the left arm 
and hand) correctly points to the shovel, but because the left hemisphere (which 
controls the verbal answer) is unaware of the snow, it confabulates an 
explanation, yet is not aware that it is confabulating. It is taking responsibility 
for an action it had never decided on and never took, but thinks that it did. 

This implies that each of the two hemispheres in a split-brain patient has its 
own consciousness. The hemispheres appear not to be aware that their body is 
effectively controlled by two brains, because they learn to coordinate with each 
other, and their decisions are sufficiently aligned and consistent that each thinks 
that the decisions of the other are its own. 

Gazzaniga’s experiment doesn’t prove that a normal individual with a 
functioning corpus callosum has two conscious half-brains, but it is suggestive 
of that possibility. While the corpus callosum allows for effective collaboration 
between the two half-brains, it doesn’t necessarily mean that they are not 
separate minds. Each one could be fooled into thinking it has made all the 
decisions, because they would all be close enough to what each would have 
decided on its own, and after all, it does have a lot of influence on each decision 
(by collaborating with the other hemisphere through the corpus callosum). So to 
each of the two minds it would seem as if it were in control. 

How would you test the conjecture that they are both conscious? One could 
assess them for neurological correlates of consciousness, which is precisely what 
Gazzaniga has done. His experiments show that each hemisphere is acting as an 
independent brain. Confabulation is not restricted to brain hemispheres; we each 
do it on a regular basis. Each hemisphere is about as intelligent as a human, so if 
we believe that a human brain is conscious, then we have to conclude that each 

hemisphere is independently conscious. We can assess the neurological 
correlates and we can conduct our own thought experiments (for example, 
considering that if two brain hemispheres without a functioning corpus callosum 
constitute two separate conscious minds, then the same would have to hold true 
for two hemispheres with a functioning connection between them), but any 
attempt at a more direct detection of consciousness in each hemisphere confronts 
us again with the lack of a scientific test for consciousness. But if we do allow 
that each hemisphere of the brain is conscious, then do we grant that the so- 
called unconscious activity in the neocortex (which constitutes the vast bulk of 
its activity) has an independent consciousness too? Or maybe it has more than 
one? Indeed, Marvin Minsky refers to the brain as a “society of mind.”— 

In another split-brain experiment the researchers showed the word “bell” to 
the right brain and “music” to the left brain. The patient was asked what word he 
saw. The left-hemisphere-controlled speech center says “music.” The subject 
was then shown a group of pictures and asked to point to a picture most closely 
related to the word he was just shown. His right-hemisphere-controlled arm 
pointed to the bell. When he was asked why he pointed to the bell, his left- 
hemisphere-controlled speech center replied, “Well, music, the last time I heard 
any music was the bells banging outside here.” He provided this explanation 
even though there were other pictures to choose from that were much more 
closely related to music. 

Again, this is a confabulation. The left hemisphere is explaining as if it 
were its own a decision that it never made and never carried out. It is not doing 
so to cover up for a friend (that is, its other hemisphere)—it genuinely thinks 
that the decision was its own. 

These reactions and decisions can extend to emotional responses. They 
asked a teenage split-brain patient—so that both hemispheres heard—“Who is 
your favorite...” and then fed the word “girlfriend” just to the right hemisphere 
through the left ear. Gazzaniga reports that the subject blushed and acted 
embarrassed, an appropriate reaction for a teenager when asked about his 
girlfriend. But the left-hemisphere-controlled speech center reported that it had 
not heard any word and asked for clarification: “My favorite what?” When asked 
again to answer the question, this time in writing, the right-hemisphere- 
controlled left hand wrote out his girlfriend’s name. 

Gazzaniga’s tests are not thought experiments but actual mind experiments. 
While they offer an interesting perspective on the issue of consciousness, they 
speak even more directly to the issue of free will. In each of these cases, one of 
the hemispheres believes that it has made a decision that it in fact never made. 
To what extent is that true for the decisions we make every day? 

Consider the case of a ten-year-old female epileptic patient. Neurosurgeon 
Itzhak Fried was performing brain surgery while she was awake (which is 
feasible because there are no pain receptors in the brain).— Whenever he 
stimulated a particular spot on her neocortex, she would laugh. At first the 
surgical team thought that they might be triggering some sort of laugh reflex, but 
they quickly realized that they were triggering the actual perception of humor. 
They had apparently found a point in her neocortex—there is obviously more 
than one—that recognizes the perception of humor. She was not just laughing— 
she actually found the situation funny, even though nothing had actually changed 
in the situation other than their having stimulated this point in her neocortex. 
When they asked her why she was laughing, she did not reply along the lines of, 
“Oh, no particular reason,” or “You just stimulated my brain,” but would 
immediately confabulate a reason. She would point to something in the room 
and try to explain why it was funny. “You guys are just so funny standing there” 
was a typical comment. 

We are apparently very eager to explain and rationalize our actions, even 
when we didn’t actually make the decisions that led to them. So just how 
responsible are we for our decisions? Consider these experiments by physiology 
professor Benjamin Libet (1916-2007) at the University of California at Davis. 
Libet had participants sit in front of a timer, EEG electrodes attached to their 
scalps. He instructed them to do simple tasks such as pushing a button or moving 
a finger. The participants were asked to note the time on the timer when they 
“first become aware of the wish or urge to act.” Tests indicated a margin of error 
of only 50 milliseconds on these assessments by the subjects. They also 
measured an average of about 200 milliseconds between the time when the 
subjects reported awareness of the urge to act and the actual act.— 

The researchers also looked at the EEG signals coming from the subjects’ 
brains. Brain activity involved in initiating the action by the motor cortex (which 
is responsible for carrying out the action) actually occurred on average about 500 
milliseconds prior to the performance of the task. That means that the motor 
cortex was preparing to carry out the task about a third of a second before the 
subject was even aware that she had made a decision to do so. 

The implications of the Libet experiments have been hotly debated. Libet 
himself concluded that our awareness of decision making appears to be an 
illusion, that “consciousness is out of the loop.” Philosopher Daniel Dennett 
commented, “The action is originally precipitated in some part of the brain, and 
off fly the signals to muscles, pausing en route to tell you, the conscious agent, 
what is going on (but like all good officials letting you, the bumbling president, 

maintain the illusion that you started it all).”— At the same time Dennett has 
questioned the timings recorded by the experiment, basically arguing that 
subjects may not really be aware of when they become aware of the decision to 
act. One might wonder: If the subject is unaware of when she is aware of making 
a decision, then who is? But the point is actually well taken—as I discussed 
earlier, what we are conscious of is far from clear. 

Indian American neuroscientist Vilayanur Subramanian “Rama” 
Ramachandran (born in 1951) explains the situation a little differently. Given 
that we have on the order of 30 billion neurons in the neocortex, there is always 
a lot going on there, and we are consciously aware of very little of it. Decisions, 
big and little, are constantly being processed by the neocortex, and proposed 
solutions bubble up to our conscious awareness. Rather than free will, 
Ramachandran suggests we should talk about “free won’t”—that is, the power to 
reject solutions proposed by the nonconscious parts of our neocortex. 

Consider the analogy to a military campaign. Army officials prepare a 
recommendation to the president. Prior to receiving the president’s approval, 
they perform preparatory work that will enable the decision to be carried out. At 
a particular moment, the proposed decision is presented to the president, who 
approves it, and the rest of the mission is then undertaken. Since the “brain” 
represented by this analogy involves the unconscious processes of the neocortex 
(that is, the officials under the president) as well as its conscious processes (the 
president), we would see neural activity as well as actual actions taking place 
prior to the official decision’s being made. We can always get into debates in a 
particular situation as to how much leeway the officials under the president 
actually gave him or her to accept or reject a recommendation, and certainly 
American presidents have done both. But it should not surprise us that mental 
activity, even in the motor cortex, would start before we were aware that there 
was a decision to be made. 

What the Libet experiments do underscore is that there is a lot of activity in 
our brains underlying our decisions that is not conscious. We already knew that 
most of what goes in the neocortex is not conscious; it should not be surprising, 
therefore, that our actions and decisions stem from both unconscious and 
conscious activity. Is this distinction important? If our decisions arise from both, 
should it matter if we sort out the conscious parts from the unconscious? Is it not 
the case that both aspects represent our brain? Are we not ultimately responsible 
for everything that goes on in our brains? “Yes, I shot the victim, but I’m not 
responsible because I wasn’t paying attention” is probably a weak defense. Even 
though there are some narrow legal grounds on which a person may not be held 
responsible for his decisions, we are generally held accountable for all of the 

choices we make. 

The observations and experiments I have cited above constitute thought 
experiments on the issue of free will, a subject that, like the topic of 
consciousness, has been debated since Plato. The term “free will” itself dates 
back to the thirteenth century, but what exactly does it mean? 

The Merriam-Webster dictionary defines it as the “freedom of humans to 
make choices that are not determined by prior causes or by divine intervention.” 
You will notice that this definition is hopelessly circular: “Free will is 
freedom....” Setting aside the idea of divine intervention’s standing in 
opposition to free will, there is one useful element in this definition, which is the 
idea of a decision’s “not [being] determined by prior causes.” I’ll come back to 
that momentarily. 

The Stanford Encyclopedia of Philosophy states that free will is the 
“capacity of rational agents to choose a course of action from among various 
alternatives.” By this definition, a simple computer is capable of free will, so it is 
less helpful than the dictionary definition. 

Wikipedia is actually a bit better. It defines free will as “the ability of agents 
to make choices free from certain kinds of constraints.... The constraint of 
dominant concern has been...determinism.” Again, it uses the circular word 
“free” in defining free will, but it does articulate what has been regarded as the 
principal enemy of free will: determinism. In that respect the Merriam-Webster 
definition above is actually similar in its reference to decisions that “are not 
determined by prior causes.” 

So what do we mean by determinism? If I put “2 + 2” into a calculator and 
it displays “4,” can I say that the calculator displayed its free will by deciding to 
display that “4”? No one would accept that as a demonstration of free will, 
because the “decision” was predetermined by the internal mechanisms of the 
calculator and the input. If I put in a more complex calculation, we still come to 
the same conclusion with regard to its lack of free will. 

How about Watson when it answers a Jeopardy! query? Although its 
deliberations are far more complex than those of the calculator, very few if any 
observers would ascribe free will to its decisions. No one human knows exactly 
how all of its programs work, but we can identify a group of people who 
collectively can describe all of its methods. More important, its output is 
determined by (1) all of its programs at the moment that the query is posed, (2) 
the query itself, (3) the state of its internal parameters that influence its 
decisions, and (4) its trillions of bytes of knowledge bases, including 
encyclopedias. Based on these four categories of information, its output is 
determined. We might speculate that presenting the same query would always 

get the same response, but Watson is programmed to learn from its experience, 
so there is the possibility that subsequent answers would be different. However, 
that does not contradict this analysis; rather, it just constitutes a change in item 3, 
the parameters that control its decisions. 

So how exactly does a human differ from Watson, such that we ascribe free 
will to the human but not to the computer program? We can identify several 
factors. Even though Watson is a better Jeopardy! player than most if not all 
humans, it is nonetheless not nearly as complex as a human neocortex. Watson 
does possess a lot of knowledge, and it does use hierarchical methods, but the 
complexity of its hierarchical thinking is still considerably less than that of a 
human. So is the difference simply one of the scale of complexity of its 
hierarchical thinking? There is an argument to be made that the issue does come 
down to this. In my discussion of the issue of consciousness I noted that my own 
leap of faith is that I would consider a computer that passed a valid Turing test to 
be conscious. The best chatbots are not able to do that today (although they are 
steadily improving), so my conclusion with regard to consciousness is a matter 
of the level of performance of the entity. Perhaps the same is true of my 
ascribing free will to it. 

Consciousness is indeed one philosophical difference between human 
brains and contemporary software programs. We consider human brains to be 
conscious, whereas we do not— yet —attribute that to software programs. Is this 
the factor we are looking for that underlies free will? 

A simple mind experiment would argue that consciousness is indeed a vital 
part of free will. Consider a situation in which someone performs an action with 
no awareness that she is doing it—it is carried out entirely by nonconscious 
activity in that person’s brain. Would we regard this to be a display of free will? 
Most people would answer no. If the action was harmful, we would probably 
still hold that person responsible but look for some recent conscious acts that 
may have caused that person to perform actions without conscious awareness, 
such as taking one drink too many, or just failing to train herself adequately to 
consciously consider her decisions before she acted on them. 

According to some commentators, the Libet experiments argued against 
free will by highlighting how much of our decision making is not conscious. 
Since there is a reasonable consensus among philosophers that free will does 
imply conscious decision making, it appears to be one prerequisite for free will. 
However, to many observers, consciousness is a necessary but not sufficient 
condition. If our decisions—conscious or otherwise—are predetermined before 
we make them, how can we say that our decisions are free? This position, which 
holds that free will and determinism are not compatible, is known as 

incompatibilism. For example, American philosopher Carl Ginet (born in 1932) 
argues that if events in the past, present, and future are determined, then we can 
be considered to have no control over them or their consequences. Our apparent 
decisions and actions are simply part of this predetermined sequence. To Ginet, 
this rules out free will. 

Not everyone regards determinism as being incompatible with the concept 
of free will, however. The compatibilists argue, essentially, that you’re free to 
decide what you want even though what you decide is or may be determined. 
Daniel Dennett, for example, argues that while the future may be determined 
from the state of the present, the reality is that the world is so intricately complex 
that we cannot possibly know what the future will bring. We can identify what 
he refers to as “expectations,” and we are indeed free to perform acts that differ 
from these expectations. We should consider how our decisions and actions 
compare to these expectations, not to a theoretically determined future that we 
cannot in fact know. That, Dennett argues, is sufficient for free will. 

Gazzaniga also articulates a compatibilist position: “We are personally 
responsible agents and are to be held accountable for our actions, even though 
we live in a determined world.”- A cynic might interpret this view as: You have 
no control over your actions, but we’ll blame you anyway. 

Some thinkers dismiss the idea of free will as an illusion. Scottish 
philosopher David Hume (1711-1776) described it as simply a “verbal” matter 
characterized by “a false sensation or seeming experience.”— German 
philosopher Arthur Schopenhauer (1788-1860) wrote that “everyone believes 
himself a priori to be perfectly free, even in his individual actions, and thinks 
that at every moment he can commence another manner of life.... But a 
posteriori, through experience, he finds to his astonishment that he is not free, 
but subjected to necessity, that in spite of all his resolutions and reflections he 
does not change his conduct, and that from the beginning of his life to the end of 
it, he must carry out the very character which he himself condemns.”— 

I would add several points here. The concept of free will—and 
responsibility, which is a closely aligned idea—is useful, and indeed vital, to 
maintaining social order, whether or not free will actually exists. Just as 
consciousness clearly exists as a meme, so too does free will. Attempts to prove 
its existence, or even to define it, may become hopelessly circular, but the reality 
is that almost everyone believes in the idea. Very substantial portions of our 
higher-level neocortex are devoted to the concept that we make free choices and 
are responsible for our actions. Whether in a strict philosophical sense that is 
true or even possible, society would be far worse off if we did not have such 


Furthermore, the world is not necessarily determined. I discussed above two 
perspectives on quantum mechanics, which differ with respect to the relationship 
of quantum fields to an observer. A popular interpretation of the observer-based 
perspective provides a role for consciousness: Particles do not resolve their 
quantum ambiguity until observed by a conscious observer. There is another split 
in the philosophy of quantum events that has a bearing on our discussion of free 
will, one that revolves around the question: Are quantum events determined or 

The most common interpretation of a quantum event is that when the wave 
function constituting a particle “collapses,” the particle’s location becomes 
specific. Over a great many such events, there will be a predictable distribution 
(which is why the wave function is considered to be a probability distribution), 
but the resolution for each such particle undergoing a collapse of its wave 
function is random. The opposing interpretation is deterministic: specifically, 
that there is a hidden variable that we are unable to detect separately, but whose 
value determines the particle’s position. The value or phase of the hidden 
variable at the moment of the wave function collapse determines the position of 
the particle. Most quantum physicists seem to favor the idea of a random 
resolution according to the probability field, but the equations for quantum 
mechanics do allow for the existence of such a hidden variable. 

Thus the world may not be determined after all. According to the 
probability wave interpretation of quantum mechanics, there is a continual 
source of uncertainty at the most basic level of reality. However, this observation 
does not necessarily resolve the concerns of the incompatibilists. It is true that 
under this interpretation of quantum mechanics, the world is not determined, but 
our concept of free will extends beyond decisions and actions that are merely 
random. Most incompatibilists would find the concept of free will to also be 
incompatible with our decisions’ being essentially accidental. Free will seems to 
imply purposeful decision making. 

Dr. Wolfram proposes a way to resolve the dilemma. His book A New Kind 
of Science (2002) presents a comprehensive view of the idea of cellular automata 
and their role in every facet of our lives. A cellular automaton is a mechanism in 
which the value of information cells is continually recomputed as a function of 
the cells near it. John von Neumann created a theoretical self-replicating 
machine called a universal constructor that was perhaps the first cellular 

Dr. Wolfram illustrates his thesis with the simplest possible cellular 
automata, a group of cells in a one-dimensional line. At each point in time, each 

cell can have one of two values: black or white. The value of each cell is 
recomputed for each cycle. The value of a cell for the next cycle is a function of 
its current value as well as the value of its two adjacent neighbors. Each cellular 
automaton is characterized by a rule that determines how we compute whether a 
cell is black or white in the next cycle. 

Consider the example of what Dr. Wolfram calls rule 222. 

rule 222 



















The eight possible combinations of value for the cell being recomputed and 
its left and right neighbors are shown in the top row. Its new value is shown in 
the bottom row. So, for example, if the cell is black and its two neighbors are 
also black, then the cell will remain black in the next generation (see the leftmost 
subrule of rule 222). If the cell is white, its left neighbor is white, and its right 
neighbor is black, then it will be changed to black in the next generation (see the 
subrule of rule 222 that is second from the right). 

The universe for this simple cellular automaton is just one row of cells. If 
we start with just one black cell in the middle and show the evolution of the cells 
over multiple generations (where each row as we move down represents a new 
generation of values), the results of rule 222 look like this: 

rule 222 




























































An automaton is based on a rule, and a rule defines whether the cell will be 
black or white based on which of the eight possible patterns exist in the current 
generation. Thus there are 2 8 = 256 possible rules. Dr. Wolfram listed all 256 
possible such automata and assigned each a Wolfram code from 0 to 255. 
Interestingly, these 256 theoretical machines have very different properties. The 
automata in what Dr. Wolfram calls class I, such as rule 222, create very 
predictable patterns. If I were to ask what the value of the middle cell was after a 
trillion trillion iterations of rule 222, you could answer easily: black. 

Much more interesting, however, are the class IV automata, illustrated by 
rule 110. 

rule 110 


□ 1 ■ 



■ 1 ■ 



0 1 



i i 



Multiple generations of this automaton look like this: 

The interesting thing about the rule 110 automaton and class IV automata in 
general is that the results are completely unpredictable. The results pass the 
strictest mathematical tests for randomness, yet they do not simply generate 
noise: There are repeating patterns, but they repeat in odd and unpredictable 
ways. If I were to ask you what the value of a particular cell was after a trillion 
trillion iterations, there would be no way to answer that question without 
actually running this machine through that many generations. The solution is 
clearly determined, because this is a very simple deterministic machine, but it is 
completely unpredictable without actually running the machine. 

Dr. Wolfram’s primary thesis is that the world is one big class IV cellular 
automaton. The reason that his book is titled A New Kind of Science is because 
this theory contrasts with most other scientific laws. If there is a satellite orbiting 
Earth, we can predict where it will be five years from now without having to run 
through each moment of a simulated process by using the relevant laws of 
gravity and solve where it will be at points in time far in the future. But the 
future state of class IV cellular automata cannot be predicted without simulating 
every step along the way. If the universe is a giant cellular automaton, as Dr. 
Wolfram postulates, there would be no computer big enough—since every 
computer would be a subset of the universe—that could run such a simulation. 
Therefore the future state of the universe is completely unknowable even though 
it is deterministic. 

Thus even though our decisions are determined (because our bodies and 
brains are part of a deterministic universe), they are nonetheless inherently 
unpredictable because we live in (and are part of) a class IV automaton. We 
cannot predict the future of a class IV automaton except to let the future unfold. 
For Dr. Wolfram, this is sufficient to allow for free will. 

We don’t have to look to the universe to see future events that are 
determined yet unpredictable. None of the scientists who have worked on 

Watson can predict what it will do, because the program is just too complex and 
varied, and its performance is based on knowledge that is far too extensive for 
any human to master. If we believe that humans exhibit free will, then it follows 
that we have to allow that future versions of Watson or Watson-like machines 
can exhibit it also. 

My own leap of faith is that I believe that humans have free will, and while 
I act as if that is the case, I am hard pressed to find examples among my own 
decisions that illustrate that. Consider the decision to write this book—I never 
made that decision. Rather, the idea of the book decided that for me. In general, I 
find myself captive to ideas that seem to implant themselves in my neocortex 
and take over. How about the decision to get married, which I made (in 
collaboration with one other person) thirty-six years ago? At the time, I had been 
following the usual program of being attracted to—and pursuing—a pretty girl. I 
then fell in love. Where is the free will in that? 

But what about the little decisions I make every day—for example, the 
specific words I choose to write in my book? I start with a blank virtual sheet of 
paper. No one is telling me what to do. There is no editor looking over my 
shoulder. My choices are entirely up to me. I am free —totally free —to write 
whatever I... 

Uh, grok ... 

Grok ? Okay, I did it—I finally applied my free will. I was going to write the 
word “want,” but I made a free decision to write something totally unexpected 
instead. This is perhaps the first time I’ve succeeded in exercising pure free will. 

Or not. 

It should be apparent that that was a display not of will, but rather of trying 
to illustrate a point (and perhaps a weak sense of humor). 

Although I share Descartes’ confidence that I am conscious, I’m not so sure 
about free will. It is difficult to escape Schopenhauer’s conclusion that “you can 
do what you will, but in any given moment of your life you can will only one 
definite thing and absolutely nothing other than that one thing.”— Nonetheless I 
will continue to act as if I have free will and to believe in it, so long as I don’t 
have to explain why. 


A philosopher once had the following dream. 

First Aristotle appeared, and the philosopher said to him, “Could you 
give me a fifteen-minute capsule sketch of your entire philosophy?” To the 
philosopher’s surprise, Aristotle gave him an excellent exposition in which 
he compressed an enormous amount of material into a mere fifteen minutes. 
But then the philosopher raised a certain objection which Aristotle couldn’t 
answer. Confounded, Aristotle disappeared. 

Then Plato appeared. The same thing happened again, and the 
philosopher’s objection to Plato was the same as his objection to Aristotle. 
Plato also couldn’t answer it and disappeared. 

Then all the famous philosophers of history appeared one by one and 
our philosopher refuted every one with the same objection. 

After the last philosopher vanished, our philosopher said to himself, “I 
know I’m asleep and dreaming all this. Yet I’ve found a universal refutation 
for all philosophical systems! Tomorrow when I wake up, I will probably 
have forgotten it, and the world will really miss something!” With an iron 
effort, the philosopher forced himself to wake up, rush over to his desk, and 
write down his universal refutation. Then he jumped back into bed with a 
sigh of relief. 

The next morning when he awoke, he went over to the desk to see 
what he had written. It was, “That’s what you say.” 

—Raymond Smullyan, as quoted by David Chalmers— 

What I wonder about ever more than whether or not I am conscious or exercise 
free will is why I happen to be conscious of the experiences and decisions of this 
one particular person who writes books, enjoys hiking and biking, takes 
nutritional supplements, and so on. An obvious answer would be, “Because 
that’s who you are.” 

That exchange is probably no more tautological than my answers above to 
questions about consciousness and free will. But actually I do have a better 
answer for why my consciousness is associated with this particular person: It is 
because that is who I created myself to be. 

A common aphorism is, “You are what you eat.” It is even more true to say, 
“You are what you think.” As we have discussed, all of the hierarchical 
structures in my neocortex that define my personality, skills, and knowledge are 
the result of my own thoughts and experiences. The people I choose to interact 
with and the ideas and projects I choose to engage in are all primary 
determinants of who I become. For that matter, what I eat also reflects the 
decisions made by my neocortex. Accepting the positive side of the free will 
duality for the moment, it is my own decisions that result in who I am. 

Regardless of how we came to be who we are, each of us has the desire for 
our identity to persist. If you didn’t have the will to survive, you wouldn’t be 
here reading this book. Every creature has that goal—it is the principal 
determinant of evolution. The issue of identity is perhaps even harder to define 
than consciousness or free will, but is arguably more important. After all, we 
need to know what we are if we seek to preserve our existence. 

Consider this thought experiment: You are in the future with technologies 
more advanced than today’s. While you are sleeping, some group scans your 
brain and picks up every salient detail. Perhaps they do this with blood cell¬ 
sized scanning machines traveling in the capillaries of your brain or with some 
other suitable noninvasive technology, but they have all of the information about 
your brain at a particular point in time. They also pick up and record any bodily 
details that might reflect on your state of mind, such as the endocrine system. 
They instantiate this “mind file” in a nonbiological body that looks and moves 
like you and has the requisite subtlety and suppleness to pass for you. In the 
morning you are informed about this transfer and you watch (perhaps without 
being noticed) your mind clone, whom we’ll call You 2. You 2 is talking about 
his or her life as if s/he were you, and relating how s/he discovered that very 
morning that s/he had been given a much more durable new version 2.0 body. 
“Hey, I kind of like this new body!” s/he exclaims. 

The first question to consider is: Is You 2 conscious? Well, s/he certainly 
seems to be. S/he passes the test I articulated earlier, in that s/he has the subtle 
cues of being a feeling, conscious person. If you are conscious, then so too is 
You 2. 

So if you were to, uh, disappear, no one would notice. You 2 would go 
around claiming to be you. All of your friends and loved ones would be content 
with the situation and perhaps pleased that you now have a more durable body 
and mental substrate than you used to have. Perhaps your more philosophically 
minded friends would express concerns, but for the most part, everybody would 
be happy, including you, or at least the person who is convincingly claiming to 
be you. 

So we don’t need your old body and brain anymore, right? Okay if we 
dispose of it? 

You’re probably not going to go along with this. I indicated that the scan 
was noninvasive, so you are still around and still conscious. Moreover your 
sense of identity is still with you, not with You 2, even though You 2 thinks s/he 
is a continuation of you. You 2 might not even be aware that you exist or ever 
existed. In fact you would not be aware of the existence of You 2 either, if we 
hadn’t told you about it. 

Our conclusion? You 2 is conscious but is a different person than you—You 
2 has a different identity. S/he is extremely similar, much more so than a mere 
genetic clone, because s/he also shares all of your neocortical patterns and 
connections. Or I should say s/he shared those patterns at the moment s/he was 
created. At that point, the two of you started to go your own ways, neocortically 
speaking. You are still around. You are not having the same experiences as You 
2. Bottom line: You 2 is not you. 

Okay, so far so good. Now consider another thought experiment—one that 
is, I believe, more realistic in terms of what the future will bring. You undergo a 
procedure to replace a very small part of your brain with a nonbiological unit. 
You’re convinced that it’s safe, and there are reports of various benefits. 

This is not so far-fetched, as it is done routinely for people with 
neurological and sensory impairments, such as the neural implant for 
Parkinson’s disease and cochlear implants for the deaf. In these cases the 
computerized device is placed inside the body but outside the brain yet 
connected into the brain (or in the case of the cochlear implants, to the auditory 
nerve). In my view the fact that the actual computer is physically placed outside 
the actual brain is not philosophically significant: We are effectively augmenting 
the brain and replacing with a computerized device those of its functions that no 
longer work properly. In the 2030s, when intelligent computerized devices will 
be the size of blood cells (and keep in mind that white blood cells are sufficiently 
intelligent to recognize and combat pathogens), we will introduce them 
noninvasively, no surgery required. 

Returning to our future scenario, you have the procedure, and as promised, 
it works just fine—certain of your capabilities have improved. (You have better 
memory, perhaps.) So are you still you? Your friends certainly think so. You 
think so. There is no good argument that you’re suddenly a different person. 
Obviously, you underwent the procedure in order to effect a change in 
something, but you are still the same you. Your identity hasn’t changed. 
Someone else’s consciousness didn’t suddenly take over your body. 

Okay, so, encouraged by these results, you now decide to have another 

procedure, this time involving a different region of the brain. The result is the 
same: You experience some improvement in capability, but you’re still you. 

It should be apparent where I am going with this. You keep opting for 
additional procedures, your confidence in the process only increasing, until 
eventually you’ve changed every part of your brain. Each time the procedure 
was carefully done to preserve all of your neocortical patterns and connections 
so that you have not lost any of your personality, skills, or memories. There was 
never a you and a You 2; there was only you. No one, including you, ever 
notices you ceasing to exist. Indeed—there you are. 

Our conclusion: You still exist. There’s no dilemma here. Everything is 


Except for this: You, after the gradual replacement process, are entirely 
equivalent to You 2 in the prior thought experiment (which I will call the scan- 
and-instantiate scenario). You, after the gradual replacement scenario, have all of 
the neocortical patterns and connections that you had originally, only in a 
nonbiological substrate, which is also true of You 2 in the scan-and-instantiate 
scenario. You, after the gradual replacement scenario, have some additional 
capabilities and greater durability than you did before the process, but this is 
likewise true of You 2 in the scan-and-instantiate process. 

But we concluded that You 2 is not you. And if you, after the gradual 
replacement process, are entirely equivalent to You 2 after the scan-and- 
instantiate process, then you after the gradual replacement process must also not 
be you. 

That, however, contradicts our earlier conclusion. The gradual replacement 
process consists of multiple steps. Each of those steps appeared to preserve 
identity, just as we conclude today that a Parkinson’s patient has the same 
identity after having had a neural implant installed.— 

It is just this sort of philosophical dilemma that leads some people to 
conclude that these replacement scenarios will never happen (even though they 
are already taking place). But consider this: We naturally undergo a gradual 
replacement process throughout our lives. Most of our cells in our body are 
continuously being replaced. (You just replaced 100 million of them in the 
course of reading the last sentence.) Cells in the inner lining of the small 
intestine turn over in about a week, as does the stomach’s protective lining. The 
life span of white blood cells ranges from a few days to a few months, depending 
on the type. Platelets last about nine days. 

Neurons persist, but their organelles and their constituent molecules turn 
over within a month.— The half-life of a neuron microtubule is about ten 

minutes; the actin filaments in the dendrites last about forty seconds; the proteins 
that provide energy to the synapses are replaced every hour; the NMDA 
receptors in synapses are relatively long-lived at five days. 

So you are completely replaced in a matter of months, which is comparable 
to the gradual replacement scenario I describe above. Are you the same person 
you were a few months ago? Certainly there are some differences. Perhaps you 
learned a few things. But you assume that your identity persists, that you are not 
continually destroyed and re-created. 

Consider a river, like the one that flows past my office. As I look out now at 
what people call the Charles River, is it the same river that I saw yesterday? 
Let’s first reflect on what a river is. The dictionary defines it is “a large natural 
stream of flowing water.” By that definition, the river I’m looking at is a 
completely different one than it was yesterday. Every one of its water molecules 
has changed, a process that happens very quickly. Greek philosopher Diogenes 
Laertius wrote in the third century AD that “you cannot step into the same river 

But that is not how we generally regard rivers. People like to look at them 
because they are symbols of continuity and stability. By the common view, the 
Charles River that I looked at yesterday is the same river I see today. Our lives 
are much the same. Fundamentally we are not the stuff that makes up our bodies 
and brains. These particles essentially flow through us in the same way that 
water molecules flow through a river. We are a pattern that changes slowly but 
has stability and continuity, even though the stuff constituting the pattern 
changes quickly. 

The gradual introduction of nonbiological systems into our bodies and 
brains will be just another example of the continual turnover of parts that 
compose us. It will not alter the continuity of our identity any more than the 
natural replacement of our biological cells does. We have already largely 
outsourced our historical, intellectual, social, and personal memories to our 
devices and the cloud. The devices we interact with to access these memories 
may not yet be inside our bodies and brains, but as they become smaller and 
smaller (and we are shrinking technology at a rate of about a hundred in 3-D 
volume per decade), they will make their way there. In any event, it will be a 
useful place to put them—we won’t lose them that way. If people do opt out of 
placing microscopic devices inside their bodies, that will be fine, as there will be 
other ways to access the pervasive cloud intelligence. 

But we come back to the dilemma I introduced earlier. You, after a period 
of gradual replacement, are equivalent to You 2 in the scan-and-instantiate 
scenario, but we decided that You 2 in that scenario does not have the same 

identity as you. So where does that leave us? 

It leaves us with an appreciation of a capability that nonbiological systems 
have that biological systems do not: the ability to be copied, backed up, and re¬ 
created. We do that routinely with our devices. When we get a new smartphone, 
we copy over all of our files, so it has much the same personality, skills, and 
memories that the old smartphone did. Perhaps it also has some new capabilities, 
but the contents of the old phone are still with us. Similarly, a program such as 
Watson is certainly backed up. If the Watson hardware were destroyed 
tomorrow, Watson would easily be re-created from its backup files stored in the 

This represents a capability in the nonbiological world that does not exist in 
the biological world. It is an advantage, not a limitation, which is one reason 
why we are so eager today to continue uploading our memories to the cloud. We 
will certainly continue in this direction, as nonbiological systems attain more and 
more of the capabilities of our biological brains. 

My resolution of the dilemma is this: It is not true that You 2 is not you—it 
is you. It is just that there are now two of you. That’s not so bad—if you think 
you are a good thing, then two of you is even better. 

What I believe will actually happen is that we will continue on the path of 
the gradual replacement and augmentation scenario until ultimately most of our 
thinking will be in the cloud. My leap of faith on identity is that identity is 
preserved through continuity of the pattern of information that makes us us. 
Continuity does allow for continual change, so whereas I am somewhat different 
than I was yesterday, I nonetheless have the same identity. However, the 
continuity of the pattern that constitutes my identity is not substrate-dependent. 
Biological substrates are wonderful—they have gotten us very far—but we are 
creating a more capable and durable substrate for very good reasons. 




And though man should remain, in some respects, the higher creature, is not 
this in accordance with the practice of nature, which allows superiority in 
some things to animals which have, on the whole, been long surpassed? Has 
she not allowed the ant and the bee to retain superiority over man in the 
organization of their communities and social arrangements, the bird in 
traversing the air, the fish in swimming, the horse in strength and fleetness, 
and the dog in self-sacrifice? 

—Samuel Butler, 1871 

There was a time, when the earth was to all appearance utterly destitute 
both of animal and vegetable life, and when according to the opinion of our 
best philosophers it was simply a hot round ball with a crust gradually 
cooling. Now if a human being had existed while the earth was in this state 
and had been allowed to see it as though it were some other world with 
which he had no concern, and if at the same time he were entirely ignorant 
of all physical science, would he not have pronounced it impossible that 
creatures possessed of anything like consciousness should be evolved from 
the seeming cinder which he was beholding? Would he not have denied that 
it contained any potentiality of consciousness? Yet in the course of time 
consciousness came. Is it not possible then that there may be even yet new 
channels dug out for consciousness, though we can detect no signs of them 
at present? 

—Samuel Butler, 1871 

When we reflect upon the manifold phases of life and consciousness which 

have been evolved already, it would be rash to say that no others can be 
developed, and that animal life is the end of all things. There was a time 
when fire was the end of all things: another when rocks and water were so. 

—Samuel Butler, 1871 

There is no security against the ultimate development of mechanical 
consciousness, in the fact of machines possessing little consciousness now. 
A mollusk has not much consciousness. Reflect upon the extraordinary 
advance which machines have made during the last few hundred years, and 
note how slowly the animal and vegetable kingdoms are advancing. The 
more highly organized machines are creatures not so much of yesterday, as 
of the last five minutes, so to speak, in comparison with past time. Assume 
for the sake of argument that conscious beings have existed for some 
twenty million years: see what strides machines have made in the last 
thousand! May not the world last twenty million years longer? If so, what 
will they not in the end become? 

—Samuel Butler, 1871 

My core thesis, which I call the law of accelerating returns (LOAR), is that 
fundamental measures of information technology follow predictable and 
exponential trajectories, belying the conventional wisdom that “you can’t predict 
the future.” There are still many things—which project, company, or technical 
standard will prevail in the marketplace, when peace will come to the Middle 
East—that remain unknowable, but the underlying price/performance and 
capacity of information has nonetheless proven to be remarkably predictable. 
Surprisingly, these trends are unperturbed by conditions such as war or peace 
and prosperity or recession. 

A primary reason that evolution created brains was to predict the future. As 
one of our ancestors walked through the savannas thousands of years ago, she 
might have noticed that an animal was progressing toward a route that she was 
taking. She would predict that if she stayed on course, their paths would 
intersect. Based on this, she decided to head in another direction, and her 
foresight proved valuable to survival. 

But such built-in predictors of the future are linear, not exponential, a 
quality that stems from the linear organization of the neocortex. Recall that the 
neocortex is constantly making predictions—what letter and word we will see 

next, whom we expect to see as we round the corner, and so on. The neocortex is 
organized with linear sequences of steps in each pattern, which means that 
exponential thinking does not come naturally to us. The cerebellum also uses 
linear predictions. When it helps us to catch a fly ball it is making a linear 
prediction about where the ball will be in our visual field of view and where our 
gloved hand should be in our visual field of view to catch it. 

As I have pointed out, there is a dramatic difference between linear and 
exponential progressions (forty steps linearly is forty, but exponentially is a 
trillion), which accounts for why my predictions stemming from the law of 
accelerating returns seem surprising to many observers at first. We have to train 
ourselves to think exponentially. When it comes to information technologies, it is 
the right way to think. 

The quintessential example of the law of accelerating returns is the 
perfectly smooth, doubly exponential growth of the price/performance of 
computation, which has held steady for 110 years through two world wars, the 
Great Depression, the Cold War, the collapse of the Soviet Union, the 
reemergence of China, the recent financial crisis, and all of the other notable 
events of the late nineteenth, twentieth, and early twenty-first centuries. Some 
people refer to this phenomenon as “Moore’s law,” but that is a misconception. 
Moore’s law—which states that you can place twice as many components on an 
integrated circuit every two years, and they run faster because they are smaller— 
is just one paradigm among many. It was in fact the fifth, not the first, paradigm 
to bring exponential growth to the price/performance of computing. 

The exponential rise of computation started with the 1890 U.S. census (the 
first to be automated) using the first paradigm of electromechanical calculation, 
decades before Gordon Moore was even born. In The Singularity Is Near I 
provide this graph through 2002, and here I update it through 2009 (see the 
graph on page 257 titled “Exponential Growth of Computing for 110 Years”). 
The smoothly predictable trajectory has continued, even through the recent 
economic downturn. 

Computation is the most important example of the law of accelerating 
returns, because of the amount of data we have for it, the ubiquity of 
computation, and its key role in ultimately revolutionizing everything we care 
about. But it is far from the only example. Once a technology becomes an 
information technology, it becomes subject to the LOAR. 

Biomedicine is becoming the most significant recent area of technology and 
industry to be transformed in this way. Progress in medicine has historically been 
based on accidental discoveries, so progress during the earlier era was linear, not 
exponential. This has nevertheless been beneficial: Life expectancy has grown 

from twenty-three years as of a thousand years ago, to thirty-seven years as of 
two hundred years ago, to close to eighty years today. With the gathering of the 
software of life—the genome—medicine and human biology have become an 
information technology. The human genome project itself was perfectly 
exponential, with the amount of genetic data doubling and the cost per base pair 
coming down by half each year since the project was initiated in 1990.^ (All the 
graphs in this chapter have been updated since The Singularity Is Near was 

Cost per Human Genome Logarithmic Plot 


The cost of sequencing a human-sized genome. 1 

Growth in Genbank 

DNA Sequence Data Logarithmic Plot 

We now have the ability to design biomedical interventions on computers 
and to test them on biological simulators, the scale and precision of which are 
also doubling every year. We can also update our own obsolete software: RNA 
interference can turn genes off, and new forms of gene therapy can add new 
genes, not just to a newborn but to a mature individual. The advance of genetic 
technologies also affects the brain reverse-engineering project, in that one 
important aspect of it is understanding how genes control brain functions such as 
creating new connections to reflect recently added cortical knowledge. There are 
many other manifestations of this integration of biology and information 
technology, as we move beyond genome sequencing to genome synthesizing. 

Another information technology that has seen smooth exponential growth is 
our ability to communicate with one another and transmit vast repositories of 
human knowledge. There are many ways to measure this phenomenon. Cooper’s 
law, which states that the total bit capacity of wireless communications in a 
given amount of radio spectrum doubles every thirty months, has held true from 
the time Guglielmo Marconi used the wireless telegraph for Morse code 
transmissions in 1897 to today’s 4G communications technologies.- According 
to Cooper’s law, the amount of information that can be transmitted over a given 
amount of radio spectrum has been doubling every two and a half years for more 
than a century. Another example is the number of bits per second transmitted on 
the Internet, which is doubling every one and a quarter years. - 

The reason I became interested in trying to predict certain aspects of 
technology is that I realized about thirty years ago that the key to becoming 

successful as an inventor (a profession I adopted when I was five years old) was 
timing. Most inventions and inventors fail not because the gadgets themselves 
don’t work, but because their timing is wrong, appearing either before all of the 
enabling factors are in place or too late, having missed the window of 

Internet Data Traffic (Global) Logarithmic Plot 


The international (country-to-country) bandwidth dedicated to the 
Internet for the world.- 

Highest Internet 
Backbone Bandwidth 

Logarithmic Plot 

10 12 
10 11 
J 10 10 

8 io 9 


*2 10 s 



a 10 7 


10 s 
10 * 

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 


The highest bandwidth (speed) of the Internet backbone. 2 

Being an engineer, about three decades ago I started to gather data on 
measures of technology in different areas. When I began this effort, I did not 
expect that it would present a clear picture, but I did hope that it would provide 
some guidance and enable me to make educated guesses. My goal was—and still 
is—to time my own technology efforts so that they will be appropriate for the 
world that exists when I complete a project—which I realized would be very 
different from the world that existed when I started. 

Consider how much and how quickly the world has changed only recently. 
Just a few years ago, people did not use social networks (Facebook, for example, 
was founded in 2004 and had 901 million monthly active users at the end of 
March 2012),- wikis, blogs, or tweets. In the 1990s most people did not use 
search engines or cell phones. Imagine the world without them. That seems like 
ancient history but was not so long ago. The world will change even more 
dramatically in the near future. 

In the course of my investigation, I made a startling discovery: If a 
technology is an information technology, the basic measures of 
price/performance and capacity (per unit of time or cost, or other resource) 
follow amazingly precise exponential trajectories. 

These trajectories outrun the specific paradigms they are based on (such as 
Moore’s law). But when one paradigm runs out of steam (for example, when 
engineers were no longer able to reduce the size and cost of vacuum tubes in the 
1950s), it creates research pressure to create the next paradigm, and so another 
S-curve of progress begins. 

The exponential portion of that next S-curve for the new paradigm then 
continues the ongoing exponential of the information technology measure. Thus 
vacuum tube-based computing in the 1950s gave way to transistors in the 1960s, 
and then to integrated circuits and Moore’s law in the late 1960s, and beyond. 
Moore’s law, in turn, will give way to three-dimensional computing, the early 
examples of which are already in place. The reason why information 
technologies are able to consistently transcend the limitations of any particular 
paradigm is that the resources required to compute or remember or transmit a bit 
of information are vanishingly small. 

We might wonder, are there fundamental limits to our ability to compute 
and transmit information, regardless of paradigm? The answer is yes, based on 
our current understanding of the physics of computation. Those limits, however, 
are not very limiting. Ultimately we can expand our intelligence trillions-fold 
based on molecular computing. By my calculations, we will reach these limits 
late in this century. 

It is important to point out that not every exponential phenomenon is an 
example of the law of accelerating returns. Some observers misconstrue the 
LOAR by citing exponential trends that are not information-based: For example, 
they point out, men’s shavers have gone from one blade to two to four, and then 
ask, where are the eight-blade shavers? Shavers are not (yet) an information 

In The Singularity Is Near, I provide a theoretical examination, including 
(in the appendix to that book) a mathematical treatment of why the LOAR is so 
remarkably predictable. Essentially, we always use the latest technology to 
create the next. Technologies build on themselves in an exponential manner, and 
this phenomenon is readily measurable if it involves an information technology. 
In 1990 we used the computers and other tools of that era to create the 
computers of 1991; in 2012 we are using current information tools to create the 
machines of 2013 and 2014. More broadly speaking, this acceleration and 
exponential growth applies to any process in which patterns of information 
evolve. So we see acceleration in the pace of biological evolution, and similar 
(but much faster) acceleration in technological evolution, which is itself an 
outgrowth of biological evolution. 

I now have a public track record of more than a quarter of a century of 
predictions based on the law of accelerating returns, starting with those 
presented in The Age of Intelligent Machines, which I wrote in the mid-1980s. 
Examples of accurate predictions from that book include: the emergence in the 
mid- to late 1990s of a vast worldwide web of communications tying together 
people around the world to one another and to all human knowledge; a great 

wave of democratization emerging from this decentralized communication 
network, sweeping away the Soviet Union; the defeat of the world chess 
champion by 1998; and many others. 

I described the law of accelerating returns, as it is applied to computation, 
extensively in The Age of Spiritual Machines, where I provided a century of data 
showing the doubly exponential progression of the price/performance of 
computation through 1998. It is updated through 2009 below. 

I recently wrote a 146-page review of the predictions I made in The Age of 
Intelligent Machines, The Age of Spiritual Machines, and The Singularity Is 
Near. (You can read the essay here by going to the link in this endnote. )-The Age 
of Spiritual Machines included hundreds of predictions for specific decades 
(2009, 2019, 2029, and 2099). For example, I made 147 predictions for 2009 in 
The Age of Spiritual Machines, which I wrote in the 1990s. Of these, 115 (78 
percent) are entirely correct as of the end of 2009; the predictions that were 
concerned with basic measurements of the capacity and price/performance of 
information technologies were particularly accurate. Another 12 (8 percent) are 
“essentially correct.” A total of 127 predictions (86 percent) are correct or 
essentially correct. (Since the predictions were made specific to a given decade, 
a prediction for 2009 was considered “essentially correct” if it came true in 2010 
or 2011.) Another 17 (12 percent) are partially correct, and 3 (2 percent) are 

Exponential Growth 
of Computing for 110 Years 

Moore's law was the fifth, not the first, paradigm to 

bring exponential growth in ccmpuling Logarithmic Plot 

10 15 -g 

1900 '10 '20 '30 '40 '50 '60 70 '80 '90 2000 '10 


Calculations per second per (constant) thousand dollars of different 
computing devices.— 

Growth in Supercomputer Power Logarithmic Plot 


Floating-point operations per second of different supercomputers.— 

Bits per Dollar 

Logarithmic Plot 

10 '° 

10 9 
10 8 
10 7 
I 10 s 
I 10 5 
10 * 

10 3 
10 2 
10 1 

1970 75 1980 ’85 1990 ’95 2000 ’05 2010 


Transistors per chip for different Intel processors.— 

Transistors per Chip 

Dynamic RAM Memory 

Logarithmic Plot 

Bits per dollar for dynamic random access memory chips 


Logaritfvnic Plot 

Random Access Memory 


Bits per dollar for random access memory chips.— 

Average Transistor Price Logarithmic Plot 

Logarithmic Plot 

Magnetic Data Storage Logarithmic Plot 

1950 1960 1970 1980 1990 2000 2010 


Bits per dollar (in constant 2000 dollars) for magnetic data storage.— 
Even the predictions that were “wrong” were not all wrong. For example, I 
judged my prediction that we would have self-driving cars to be wrong, even 
though Google has demonstrated self-driving cars, and even though in October 
2010 four driverless electric vans successfully concluded a 13,000-kilometer test 
drive from Italy to China.— Experts in the field currently predict that these 
technologies will be routinely available to consumers by the end of this decade. 
Exponentially expanding computational and communication technologies 

all contribute to the project to understand and re-create the methods of the 
human brain. This effort is not a single organized project but rather the result of 
a great many diverse projects, including detailed modeling of constituents of the 
brain ranging from individual neurons to the entire neocortex, the mapping of 
the “connectome” (the neural connections in the brain), simulations of brain 
regions, and many others. All of these have been scaling up exponentially. Much 
of the evidence presented in this book has only become available recently—for 
example, the 2012 Wedeen study discussed in chapter 4 that showed the very 
orderly and “simple” (to quote the researchers) gridlike pattern of the 
connections in the neocortex. The researchers in that study acknowledge that 
their insight (and images) only became feasible as the result of new high- 
resolution imaging technology. 

Brain scanning technologies are improving in resolution, spatial and 
temporal, at an exponential rate. Different types of brain scanning methods being 
pursued range from completely noninvasive methods that can be used with 
humans to more invasive or destructive methods on animals. 

MRI (magnetic resonance imaging), a noninvasive imaging technique with 
relatively high temporal resolution, has steadily improved at an exponential 
pace, to the point that spatial resolutions are now close to 100 microns 
(millionths of a meter). 



In Vivo 




In Vitro 


A Venn diagram of brain imaging methods.— 

Spatial Resolution 


Time Resolution or Duration 
Tools for imaging the brain.— 

MR I Spatial Resolution Logarithmic Plot 


MRI spatial resolution in microns.— 

Spatial Resolution of Destructive 

Brain Imaging Techniques Logarithmic Plot 



1983 1987 1991 1995 1999 2003 2007 


Spatial resolution of destructive imaging techniques.— 


Nondestructive Brain Imaging 
Resolution in Animals Logarithmic Plot 


Spatial resolution of nondestructive imaging techniques in animals.— 
Destructive imaging, which is performed to collect the connectome (map of 
all interneuronal connections) in animal brains, has also improved at an 
exponential pace. Current maximum resolution is around four nanometers, 
which is sufficient to see individual connections. 

Artificial intelligence technologies such as natural-language-understanding 

systems are not necessarily designed to emulate theorized principles of brain 
function, but rather for maximum effectiveness. Given this, it is notable that the 
techniques that have won out are consistent with the principles I have outlined in 
this book: self-organizing, hierarchical recognizers of invariant self-associative 
patterns with redundancy and up-and-down predictions. These systems are also 
scaling up exponentially, as Watson has demonstrated. 

A primary purpose of understanding the brain is to expand our toolkit of 
techniques to create intelligent systems. Although many AI researchers may not 
fully appreciate this, they have already been deeply influenced by our knowledge 
of the principles of the operation of the brain. Understanding the brain also helps 
us to reverse brain dysfunctions of various kinds. There is, of course, another 
key goal of the project to reverse-engineer the brain: understanding who we are. 



If a machine can prove indistinguishable from a human, we should award it 
the respect we would to a human—we should accept that it has a mind. 

—Stevan Harnad 

T he most significant source of objection to my thesis on the law of accelerating 
returns and its application to the amplification of human intelligence stems from 
the linear nature of human intuition. As I described earlier, each of the several 
hundred million pattern recognizers in the neocortex processes information 
sequentially. One of the implications of this organization is that we have linear 
expectations about the future, so critics apply their linear intuition to information 
phenomena that are fundamentally exponential. 

I call objections along these lines “criticism from incredulity,” in that 
exponential projections seem incredible given our linear predilection, and they 
take a variety of forms. Microsoft cofounder Paul Allen (born in 1953) and his 
colleague Mark Greaves recently articulated several of them in an essay titled 
“The Singularity Isn’t Near” published in Technology Review magazine.- While 
my response here is to Allen’s particular critiques, they represent a typical range 
of objections to the arguments I’ve made, especially with regard to the brain. 
Although Allen references The Singularity Is Near in the title of his essay, his 
only citation in the piece is to an essay I wrote in 2001 (“The Law of 
Accelerating Returns”). Moreover, his article does not acknowledge or respond 
to arguments I actually make in the book. Unfortunately, I find this often to be 
the case with critics of my work. 

When The Age of Spiritual Machines was published in 1999, augmented 
later by the 2001 essay, it generated several lines of criticism, such as: Moore’s 
law will come to an end; hardware capability may be expanding exponentially 
but software is stuck in the mud; the brain is too complicated; there are 
capabilities in the brain that inherently cannot be replicated in software; and 

several others. One of the reasons I wrote The Singularity Is Near was to 
respond to those critiques. 

I cannot say that Allen and similar critics would necessarily have been 
convinced by the arguments I made in that book, but at least he and others could 
have responded to what I actually wrote. Allen argues that “the Law of 
Accelerating Returns (LOAR) not a physical law.” I would point out that 
most scientific laws are not physical laws, but result from the emergent 
properties of a large number of events at a lower level. A classic example is the 
laws of thermodynamics (LOT). If you look at the mathematics underlying the 
LOT, it models each particle as following a random walk, so by definition we 
cannot predict where any particular particle will be at any future time. Yet the 
overall properties of the gas are quite predictable to a high degree of precision, 
according to the laws of thermodynamics. So it is with the law of accelerating 
returns: Each technology project and contributor is unpredictable, yet the overall 
trajectory, as quantified by basic measures of price/performance and capacity, 
nonetheless follows a remarkably predictable path. 

If computer technology were being pursued by only a handful of 
researchers, it would indeed be unpredictable. But it’s the product of a 
sufficiently dynamic system of competitive projects that a basic measure of its 
price/performance, such as calculations per second per constant dollar, follows a 
very smooth exponential path, dating back to the 1890 American census as I 
noted in the previous chapter . While the theoretical basis for the LOAR is 
presented extensively in The Singularity Is Near, the strongest case for it is made 
by the extensive empirical evidence that I and others present. 

Allen writes that “these ‘laws’ work until they don’t.” Here he is confusing 
paradigms with the ongoing trajectory of a basic area of information technology. 
If we were examining, for example, the trend of creating ever smaller vacuum 
tubes—the paradigm for improving computation in the 1950s—it’s true that it 
continued until it didn’t. But as the end of this particular paradigm became clear, 
research pressure grew for the next paradigm. The technology of transistors kept 
the underlying trend of the exponential growth of price/performance of 
computation going, and that led to the fifth paradigm (Moore’s law) and the 
continual compression of features on integrated circuits. There have been regular 
predictions that Moore’s law will come to an end. The semiconductor industry’s 
“International Technology Roadmap for Semiconductors” projects seven- 
nanometer features by the early 2020s. 2 At that point key features will be the 
width of thirty-five carbon atoms, and it will be difficult to continue shrinking 
them any farther. However, Intel and other chip makers are already taking the 
first steps toward the sixth paradigm, computing in three dimensions, to continue 

exponential improvement in price/performance. Intel projects that three- 
dimensional chips will be mainstream by the teen years; three-dimensional 
transistors and 3-D memory chips have already been introduced. This sixth 
paradigm will keep the LOAR going with regard to computer price/performance 
to a time later in this century when a thousand dollars’ worth of computation will 
be trillions of times more powerful than the human brain.^ (It appears that Allen 
and I are at least in agreement on what level of computation is required to 
functionally simulate the human brain.)- 

Allen then goes on to give the standard argument that software is not 
progressing in the same exponential manner as hardware. In The Singularity Is 
Near I addressed this issue at length, citing different methods of measuring 
complexity and capability in software that do demonstrate a similar exponential 
growth.- One recent study (“Report to the President and Congress, Designing a 
Digital Future: Federally Funded Research and Development in Networking and 
Information Technology,” by the President’s Council of Advisors on Science and 
Technology) states the following: 

Even more remarkable—and even less widely understood—is that in 
many areas, performance gains due to improvements in algorithms have 
vastly exceeded even the dramatic performance gains due to increased 
processor speed. The algorithms that we use today for speech recognition, 
for natural language translation, for chess playing, for logistics planning, 
have evolved remarkably in the past decade.... Here is just one example, 
provided by Professor Martin Grotschel of Konrad-Zuse-Zentrum fur 
Informationstechnik Berlin. Grotschel, an expert in optimization, observes 
that a benchmark production planning model solved using linear 
programming would have taken 82 years to solve in 1988, using the 
computers and the linear programming algorithms of the day. Fifteen years 
later—in 2003—this same model could be solved in roughly 1 minute, an 
improvement by a factor of roughly 43 million. Of this, a factor of roughly 
1,000 was due to increased processor speed, whereas a factor of roughly 
43,000 was due to improvements in algorithms! Grotschel also cites an 
algorithmic improvement of roughly 30,000 for mixed integer 
programming between 1991 and 2008. The design and analysis of 
algorithms, and the study of the inherent computational complexity of 
problems, are fundamental subfields of computer science. 

Note that the linear programming that Grotschel cites above as having 
benefited from an improvement in performance of 43 million to 1 is the 

mathematical technique that is used to optimally assign resources in a 
hierarchical memory system such as HHMM that I discussed earlier. I cite many 
other similar examples like this in The Singularity Is Near.- 

Regarding AI, Allen is quick to dismiss IBM’s Watson, an opinion shared 
by many other critics. Many of these detractors don’t know anything about 
Watson other than the fact that it is software running on a computer (albeit a 
parallel one with 720 processor cores). Allen writes that systems such as Watson 
“remain brittle, their performance boundaries are rigidly set by their internal 
assumptions and defining algorithms, they cannot generalize, and they 
frequently give nonsensical answers outside of their specific areas.” 

First of all, we could make a similar observation about humans. I would 
also point out that Watson’s “specific areas” include all of Wikipedia plus many 
other knowledge bases, which hardly constitute a narrow focus. Watson deals 
with a vast range of human knowledge and is capable of dealing with subtle 
forms of language, including puns, similes, and metaphors in virtually all fields 
of human endeavor. It’s not perfect, but neither are humans, and it was good 
enough to be victorious on Jeopardy! over the best human players. 

Allen argues that Watson was assembled by the scientists themselves, 
building each link of narrow knowledge in specific areas. This is simply not true. 
Although a few areas of Watson’s data were programmed directly, Watson 
acquired the significant majority of its knowledge on its own by reading natural- 
language documents such as Wikipedia. That represents its key strength, as does 
its ability to understand the convoluted language in Jeopardy! queries (answers 
in search of a question). 

As I mentioned earlier, much of the criticism of Watson is that it works 
through statistical probabilities rather than “true” understanding. Many readers 
interpret this to mean that Watson is merely gathering statistics on word 
sequences. The term “statistical information” in the case of Watson actually 
refers to distributed coefficients and symbolic connections in self-organizing 
methods such as hierarchical hidden Markov models. One could just as easily 
dismiss the distributed neurotransmitter concentrations and redundant 
connection patterns in the human cortex as “statistical information.” Indeed we 
resolve ambiguities in much the same way that Watson does—by considering the 
likelihood of different interpretations of a phrase. 

Allen continues, “Every structure [in the brain] has been precisely shaped 
by millions of years of evolution to do a particular thing, whatever it might be. It 
is not like a computer, with billions of identical transistors in regular memory 
arrays that are controlled by a CPU with a few different elements. In the brain 
every individual structure and neural circuit has been individually refined by 

evolution and environmental factors.” 

This contention that every structure and neural circuit in the brain is unique 
and there by design is simply impossible, for it would mean that the blueprint of 
the brain would require hundreds of trillions of bytes of information. The brain’s 
structural plan (like that of the rest of the body) is contained in the genome, and 
the brain itself cannot contain more design information than the genome. Note 
that epigenetic information (such as the peptides controlling gene expression) 
does not appreciably add to the amount of information in the genome. 
Experience and learning do add significantly to the amount of information 
contained in the brain, but the same can be said of AI systems like Watson. I 
show in The Singularity Is Near that, after lossless compression (due to massive 
redundancy in the genome), the amount of design information in the genome is 
about 50 million bytes, roughly half of which (that is, about 25 million bytes) 
pertains to the brain.- That’s not simple, but it is a level of complexity we can 
deal with and represents less complexity than many software systems in the 
modern world. Moreover much of the brain’s 25 million bytes of genetic design 
information pertain to the biological requirements of neurons, not to their 
information-proce ssing algorithms. 

How do we arrive at on the order of 100 to 1,000 trillion connections in the 
brain from only tens of millions of bytes of design information? Obviously, the 
answer is through massive redundancy. Dharmendra Modha, manager of 
Cognitive Computing for IBM Research, writes that “neuroanatomists have not 
found a hopelessly tangled, arbitrarily connected network, completely 
idiosyncratic to the brain of each individual, but instead a great deal of repeating 
structure within an individual brain and a great deal of homology across 
species.... The astonishing natural reconfigurability gives hope that the core 
algorithms of neurocomputation are independent of the specific sensory or motor 
modalities and that much of the observed variation in cortical structure across 
areas represents a refinement of a canonical circuit; it is indeed this canonical 
circuit we wish to reverse engineer. 

Allen argues in favor of an inherent “complexity brake that would 
necessarily limit progress in understanding the human brain and replicating its 
capabilities,” based on his notion that each of the approximately 100 to 1,000 
trillion connections in the human brain is there by explicit design. His 
“complexity brake” confuses the forest with the trees. If you want to understand, 
model, simulate, and re-create a pancreas, you don’t need to re-create or simulate 
every organelle in every pancreatic islet cell. You would want instead to 
understand one islet cell, then abstract its basic functionality as it pertains to 

insulin control, and then extend that to a large group of such cells. This 
algorithm is well understood with regard to islet cells. There are now artificial 
pancreases that utilize this functional model being tested. Although there is 
certainly far more intricacy and variation in the brain than in the massively 
repeated islet cells of the pancreas, there is nonetheless massive repetition of 
functions, as I have described repeatedly in this book. 

Critiques along the lines of Allen’s also articulate what I call the “scientist’s 
pessimism.” Researchers working on the next generation of a technology or of 
modeling a scientific area are invariably struggling with that immediate set of 
challenges, so if someone describes what the technology will look like in ten 
generations, their eyes glaze over. One of the pioneers of integrated circuits was 
recalling for me recently the struggles to go from 10-micron (10,000 
nanometers) feature sizes to 5-micron (5,000 nanometers) features over thirty 
years ago. The scientists were cautiously confident of reaching this goal, but 
when people predicted that someday we would actually have circuitry with 
feature sizes under 1 micron (1,000 nanometers), most of them, focused on their 
own goal, thought that was too wild to contemplate. Objections were made 
regarding the fragility of circuitry at that level of precision, thermal effects, and 
so on. Today Intel is starting to use chips with 22-nanometer gate lengths. 

We witnessed the same sort of pessimism with respect to the Human 
Genome Project. Halfway through the fifteen-year effort, only 1 percent of the 
genome had been collected, and critics were proposing basic limits on how 
quickly it could be sequenced without destroying the delicate genetic structures. 
But thanks to the exponential growth in both capacity and price/performance, the 
project was finished seven years later. The project to reverse-engineer the human 
brain is making similar progress. It is only recently, for example, that we have 
reached a threshold with noninvasive scanning techniques so that we can see 
individual interneuronal connections forming and firing in real time. Much of the 
evidence I have presented in this book was dependent on such developments and 
has only recently been available. 

Allen describes my proposal about reverse-engineering the human brain as 
simply scanning the brain to understand its fine structure and then simulating an 
entire brain “bottom up” without comprehending its information-processing 
methods. This is not my proposition. We do need to understand in detail how 
individual types of neurons work, and then gather information about how 
functional modules are connected. The functional methods that are derived from 
this type of analysis can then guide the development of intelligent systems. 
Basically, we are looking for biologically inspired methods that can accelerate 
work in AI, much of which has progressed without significant insight as to how 

the brain performs similar functions. From my own work in speech recognition, I 
know that our work was greatly accelerated when we gained insights as to how 
the brain prepares and transforms auditory information. 

The way that the massively redundant structures in the brain differentiate is 
through learning and experience. The current state of the art in AI does in fact 
enable systems to also learn from their own experience. The Google self-driving 
cars learn from their own driving experience as well as from data from Google 
cars driven by human drivers; Watson learned most of its knowledge by reading 
on its own. It is interesting to note that the methods deployed today in AI have 
evolved to be mathematically very similar to the mechanisms in the neocortex. 

Another objection to the feasibility of “strong AI” (artificial intelligence at 
human levels and beyond) that is often raised is that the human brain makes 
extensive use of analog computing, whereas digital methods inherently cannot 
replicate the gradations of value that analog representations can embody. It is 
true that one bit is either on or off, but multiple-bit words easily represent 
multiple gradations and can do so to any desired degree of accuracy. This is, of 
course, done all the time in digital computers. As it is, the accuracy of analog 
information in the brain (synaptic strength, for example) is only about one level 
within 256 levels that can be represented by eight bits. 

In chapter 9 I cited Roger Penrose and Stuart Hameroff’s objection, which 
concerned microtubules and quantum computing. Recall that they claim that the 
microtubule structures in neurons are doing quantum computing, and since it is 
not possible to achieve that in computers, the human brain is fundamentally 
different and presumably better. As I argued earlier, there is no evidence that 
neuronal microtubules are carrying out quantum computation. Humans in fact do 
a very poor job of solving the kinds of problems that a quantum computer would 
excel at (such as factoring large numbers). And if any of this proved to be true, 
there would be nothing barring quantum computing from also being used in our 

John Searle is famous for introducing a thought experiment he calls “the 
Chinese room,” an argument I discuss in detail in The Singularity Is Near.- In 
short, it involves a man who takes in written questions in Chinese and then 
answers them. In order to do this, he uses an elaborate rulebook. Searle claims 
that the man has no true understanding of Chinese and is not “conscious” of the 
language (as he does not understand the questions or the answers) despite his 
apparent ability to answer questions in Chinese. Searle compares this to a 
computer and concludes that a computer that could answer questions in Chinese 
(essentially passing a Chinese Turing test) would, like the man in the Chinese 
room, have no real understanding of the language and no consciousness of what 

it was doing. 

There are a few philosophical sleights of hand in Searle’s argument. For one 
thing, the man in this thought experiment is comparable only to the central 
processing unit (CPU) of a computer. One could say that a CPU has no true 
understanding of what it is doing, but the CPU is only part of the structure. In 
Searle’s Chinese room, it is the man with his rulebook that constitutes the whole 
system. That system does have an understanding of Chinese; otherwise it would 
not be capable of convincingly answering questions in Chinese, which would 
violate Searle’s assumption for this thought experiment. 

The attractiveness of Searle’s argument stems from the fact that it is 
difficult today to infer true understanding and consciousness in a computer 
program. The problem with his argument, however, is that you can apply his 
own line of reasoning to the human brain itself. Each neocortical pattern 
recognizer—indeed, each neuron and each neuronal component—is following an 
algorithm. (After all, these are molecular mechanisms that follow natural law.) If 
we conclude that following an algorithm is inconsistent with true understanding 
and consciousness, then we would have to also conclude that the human brain 
does not exhibit these qualities either. You can take John Searle’s Chinese room 
argument and simply substitute “manipulating interneuronal connections and 
synaptic strengths” for his words “manipulating symbols” and you will have a 
convincing argument to the effect that human brains cannot truly understand 

Another line of argument comes from the nature of nature, which has 
become a new sacred ground for many observers. For example, New Zealand 
biologist Michael Denton (born in 1943) sees a profound difference between the 
design principles of machines and those of biology. Denton writes that natural 
entities are “self-organizing,... self-referential,... self-replicating,...reciprocal,... 
self-formative, and...holistic.”— He claims that such biological forms can only 
be created through biological processes and that these forms are thereby 
“immutable,...impenetrable, and...fundamental” realities of existence, and are 
therefore basically a different philosophical category from machines. 

The reality, as we have seen, is that machines can be designed using these 
same principles. Learning the specific design paradigms of nature’s most 
intelligent entity—the human brain—is precisely the purpose of the brain 
reverse-engineering project. It is also not true that biological systems are 
completely “holistic,” as Denton puts it, nor, conversely, do machines need to be 
completely modular. We have clearly identified hierarchies of units of 
functionality in natural systems, especially the brain, and AI systems are using 
comparable methods. 

It appears to me that many critics will not be satisfied until computers 
routinely pass the Turing test, but even that threshold will not be clear-cut. 
Undoubtedly, there will be controversy as to whether claimed Turing tests that 
have been administered are valid. Indeed, I will probably be among those critics 
disparaging early claims along these lines. By the time the arguments about the 
validity of a computer passing the Turing test do settle down, computers will 
have long since surpassed unenhanced human intelligence. 

My emphasis here is on the word “unenhanced,” because enhancement is 
precisely the reason that we are creating these “mind children,” as Hans 
Moravec calls them.— Combining human-level pattern recognition with the 
inherent speed and accuracy of computers will result in very powerful abilities. 
But this is not an alien invasion of intelligent machines from Mars—we are 
creating these tools to make ourselves smarter. I believe that most observers will 
agree with me that this is what is unique about the human species: We build 
these tools to extend our own reach. 


The picture’s pretty bleak, gentlemen...The world’s climates are changing, 
the mammals are taking over, and we all have a brain about the size of a 

—Dinosaurs talking, in The Far Side by Gary Larson 

Intelligence may be defined as the ability to solve problems with limited 
resources, in which a key such resource is time. Thus the ability to more quickly 
solve a problem like finding food or avoiding a predator reflects greater power 
of intellect. Intelligence evolved because it was useful for survival—a fact that 
may seem obvious, but one with which not everyone agrees. As practiced by our 
species, it has enabled us not only to dominate the planet but to steadily improve 
the quality of our lives. This latter point, too, is not apparent to everyone, given 
that there is a widespread perception today that life is only getting worse. For 
example, a Gallup poll released on May 4, 2011, revealed that only “44 percent 
of Americans believed that today’s youth will have a better life than their 

If we look at the broad trends, not only has human life expectancy 

quadrupled over the last millennium (and more than doubled in the last two 

centuries),- but per capita GDP (in constant current dollars) has gone from 

hundreds of dollars in 1800 to thousands of dollars today, with even more 

pronounced trends in the developed world. 3 Only a handful of democracies 
existed a century ago, whereas they are the norm today. For a historical 
perspective on how far we have advanced, I suggest people read Thomas 
Hobbes’s Leviathan (1651), in which he describes the “life of man” as “solitary, 
poor, nasty, brutish, and short.” For a modern perspective, the recent book 
Abundance (2012), by X-Prize Foundation founder (and cofounder with me of 
Singularity University) Peter Diamandis and science writer Steven Kotler, 

documents the extraordinary ways in which life today has steadily improved in 
every dimension. Steven Pinker’s recent The Better Angels of Our Nature: Why 
Violence Has Declined (2011) painstakingly documents the steady rise of 
peaceful relations between people and peoples. American lawyer, entrepreneur, 
and author Martine Rothblatt (born in 1954) documents the steady improvement 
in civil rights, noting, for example, how in a couple of decades same-sex 
marriage went from being legally recognized nowhere in the world to being 
legally accepted in a rapidly growing number of jurisdictions.- 

A primary reason that people believe that life is getting worse is because 
our information about the problems of the world has steadily improved. If there 
is a battle today somewhere on the planet, we experience it almost as if we were 
there. During World War II, tens of thousands of people might perish in a battle, 
and if the public could see it at all it was in a grainy newsreel in a movie theater 
weeks later. During World War I a small elite could read about the progress of 
the conflict in the newspaper (without pictures). During the nineteenth century 
there was almost no access to news in a timely fashion for anyone. 

The advancement we have made as a species due to our intelligence is 
reflected in the evolution of our knowledge, which includes our technology and 
our culture. Our various technologies are increasingly becoming information 
technologies, which inherently continue to progress in an exponential manner. It 
is through such technologies that we are able to address the grand challenges of 
humanity, such as maintaining a healthy environment, providing the resources 
for a growing population (including energy, food, and water), overcoming 
disease, vastly extending human longevity, and eliminating poverty. It is only by 
extending ourselves with intelligent technology that we can deal with the scale 
of complexity needed to address these challenges. 

These technologies are not the vanguard of an intelligent invasion that will 
compete with and ultimately displace us. Ever since we picked up a stick to 
reach a higher branch, we have used our tools to extend our reach, both 
physically and mentally. That we can take a device out of our pocket today and 
access much of human knowledge with a few keystrokes extends us beyond 
anything imaginable by most observers only a few decades ago. The “cell 
phone” (the term is placed in quotes because it is vastly more than a phone) in 
my pocket is a million times less expensive yet thousands of times more 
powerful than the computer all the students and professors at MIT shared when I 
was an undergraduate there. That’s a several billion-fold increase in 
price/performance over the last forty years, an escalation we will see again in the 
next twenty-five years, when what used to fit in a building, and now fits in your 
pocket, will fit inside a blood cell. 

In this way we will merge with the intelligent technology we are creating. 
Intelligent nanobots in our bloodstream will keep our biological bodies healthy 
at the cellular and molecular levels. They will go into our brains noninvasively 
through the capillaries and interact with our biological neurons, directly 
extending our intelligence. This is not as futuristic as it may sound. There are 
already blood cell-sized devices that can cure type I diabetes in animals or 
detect and destroy cancer cells in the bloodstream. Based on the law of 
accelerating returns, these technologies will be a billion times more powerful 
within three decades than they are today. 

I already consider the devices I use and the cloud of computing resources to 
which they are virtually connected as extensions of myself, and feel less than 
complete if I am cut off from these brain extenders. That is why the one-day 
strike by Google, Wikipedia, and thousands of other Web sites against the SOPA 
(Stop Online Piracy Act) on January 18, 2012, was so remarkable: I felt as if part 
of my brain were going on strike (although I and others did find ways to access 
these online resources). It was also an impressive demonstration of the political 
power of these sites as the bill—which looked as if it was headed for ratification 
—was instantly killed. But more important, it showed how thoroughly we have 
already outsourced parts of our thinking to the cloud of computing. It is already 
part of who we are. Once we routinely have intelligent nonbiological intelligence 
in our brains, this augmentation—and the cloud it is connected to—will continue 
to grow in capability exponentially. 

The intelligence we will create from the reverse-engineering of the brain 
will have access to its own source code and will be able to rapidly improve itself 
in an accelerating iterative design cycle. Although there is considerable plasticity 
in the biological human brain, as we have seen, it does have a relatively fixed 
architecture, which cannot be significantly modified, as well as a limited 
capacity. We are unable to increase its 300 million pattern recognizers to, say, 
400 million unless we do so nonbiologically. Once we can achieve that, there 
will be no reason to stop at a particular level of capability. We can go on to make 
it a billion pattern recognizers, or a trillion. 

From quantitative improvement comes qualitative advance. The most 
important evolutionary advance in Homo sapiens was quantitative: the 
development of a larger forehead to accommodate more neocortex. Greater 
neocortical capacity enabled this new species to create and contemplate thoughts 
at higher conceptual levels, resulting in the establishment of all the varied fields 
of art and science. As we add more neocortex in a nonbiological form, we can 
expect ever higher qualitative levels of abstraction. 

British mathematician Irvin J. Good, a colleague of Alan Turing’s, wrote in 

1965 that “the first ultraintelligent machine is the last invention that man need 
ever make.” He defined such a machine as one that could surpass the 
“intellectual activities of any man however clever” and concluded that “since the 
design of machines is one of these intellectual activities, an ultraintelligent 
machine could design even better machines; there would then unquestionably be 
an 'intelligence explosion.’” 

The last invention that biological evolution needed to make—the neocortex 
—is inevitably leading to the last invention that humanity needs to make—truly 
intelligent machines—and the design of one is inspiring the other. Biological 
evolution is continuing but technological evolution is moving a million times 
faster than the former. According to the law of accelerating returns, by the end of 
this century we will be able to create computation at the limits of what is 
possible, based on the laws of physics as applied to computation.- We call matter 
and energy organized in this way “computronium,” which is vastly more 
powerful pound per pound than the human brain. It will not just be raw 
computation but will be infused with intelligent algorithms constituting all of 
human-machine knowledge. Over time we will convert much of the mass and 
energy in our tiny corner of the galaxy that is suitable for this purpose to 
computronium. Then, to keep the law of accelerating returns going, we will need 
to spread out to the rest of the galaxy and universe. 

If the speed of light indeed remains an inexorable limit, then colonizing the 
universe will take a long time, given that the nearest star system to Earth is four 
light-years away. If there are even subtle means to circumvent this limit, our 
intelligence and technology will be sufficiently powerful to exploit them. This is 
one reason why the recent suggestion that the muons that traversed the 730 
kilometers from the CERN accelerator on the Swiss-French border to the Gran 
Sasso Laboratory in central Italy appeared to be moving faster than the speed of 
light was such potentially significant news. This particular observation appears 
to be a false alarm, but there are other possibilities to get around this limit. We 
do not even need to exceed the speed of light if we can find shortcuts to other 
apparently faraway places through spatial dimensions beyond the three with 
which we are familiar. Whether we are able to surpass or otherwise get around 
the speed of light as a limit will be the key strategic issue for the human-machine 
civilization at the beginning of the twenty-second century. 

Cosmologists argue about whether the world will end in fire (a big crunch 
to match the big bang) or ice (the death of the stars as they spread out into an 
eternal expansion), but this does not take into account the power of intelligence, 
as if its emergence were just an entertaining sideshow to the grand celestial 
mechanics that now rule the universe. How long will it take for us to spread our 

intelligence in its nonbiological form throughout the universe? If we can 
transcend the speed of light—admittedly a big if—for example, by using 
wormholes through space (which are consistent with our current understanding 
of physics), it could be achieved within a few centuries. Otherwise, it will likely 
take much longer. In either scenario, waking up the universe, and then 
intelligently deciding its fate by infusing it with our human intelligence in its 
nonbiological form, is our destiny. 



L Here is one sentence from One Hundred Years of Solitude by 
Gabriel Garcia Marquez: 

Aureliano Segundo was not aware of the singsong until the 
following day after breakfast when he felt himself being bothered by a 
buzzing that was by then more fluid and louder than the sound of the 
rain, and it was Fernanda, who was walking throughout the house 
complaining that they had raised her to be a queen only to have her 
end up as a servant in a madhouse, with a lazy, idolatrous, libertine 
husband who lay on his back waiting for bread to rain down from 
heaven while she was straining her kidneys trying to keep afloat a 
home held together with pins where there was so much to do, so much 
to bear up under and repair from the time God gave his morning 
sunlight until it was time to go to bed that when she got there her eyes 
were full of ground glass, and yet no one ever said to her, “Good 
morning, Fernanda, did you sleep well?,” nor had they asked her, even 
out of courtesy, why she was so pale or why she awoke with purple 
rings under her eyes in spite of the fact that she expected it, of course, 
from a family that had always considered her a nuisance, an old rag, a 
booby painted on the wall, and who were always going around saying 
things against her behind her back, calling her churchmouse, calling 
her Pharisee, calling her crafty, and even Amaranta, may she rest in 
peace, had said aloud that she was one of those people who could not 
tell their rectums from their ashes, God have mercy, such words, and 
she had tolerated everything with resignation because of the Holy 
Father, but she had not been able to tolerate it any more when that evil 
Jose Arcadio Segundo said that the damnation of the family had come 
when it opened its doors to a stuck-up highlander, just imagine, a 
bossy highlander, Lord save us, a highlander daughter of evil spit of 
the same stripe as the highlanders the government sent to kill workers, 
you tell me, and he was referring to no one but her, the godchild of the 
Duke of Alba, a lady of such lineage that she made the liver of 
presidents’ wives quiver, a noble dame of fine blood like her, who had 
the right to sign eleven peninsular names and who was the only mortal 
creature in that town full of bastards who did not feel all confused at 

the sight of sixteen pieces of silverware, so that her adulterous husband 
could die of laughter afterward and say that so many knives and forks 
and spoons were not meant for a human being but for a centipede, and 
the only one who could tell with her eyes closed when the white wine 
was served and on what side and in which glass and when the red wine 
and on what side and in which glass and not like that peasant of an 
Amaranta, may she rest in peace, who thought that white wine was 
served in the daytime and red wine at night, and the only one on the 
whole coast who could take pride in the fact that she took care of her 
bodily needs only in golden chamberpots, so that Colonel Aureliano 
Buendia, may he rest in peace, could have the effrontery to ask her 
with his Masonic ill humor where she had received that privilege and 
whether she did not shit shit but shat sweet basil, just imagine, with 
those very words, and so that Renata, her own daughter, who through 
an oversight had seen her stool in the bedroom, had answered that 
even if the pot was all gold and with a coat of arms, what was inside 
was pure shit, physical shit, and worse even than any other kind 
because it was stuck-up highland shit, just imagine, her own daughter, 
so that she never had any illusions about the rest of the family, but in 
any case she had the right to expect a little more consideration from 
her husband because, for better or for worse, he was her consecrated 
spouse, her helpmate, her legal despoiler, who took upon himself of 
his own free and sovereign will the grave responsibility of taking her 
away from her paternal home, where she never wanted for or suffered 
from anything, where she wove funeral wreaths as a pastime, since her 
godfather had sent a letter with his signature and the stamp of his ring 
on the sealing wax simply to say that the hands of his goddaughter 
were not meant for tasks of this world except to play the clavichord, 
and, nevertheless, her insane husband had taken her from her home 
with all manner of admonitions and warnings and had brought her to 
that frying pan of hell where a person could not breathe because of the 
heat, and before she had completed her Pentecostal fast he had gone 
off with his wandering trunks and his wastrel’s accordion to loaf in 
adultery with a wretch of whom it was only enough to see her behind, 
well, that’s been said, to see her wiggle her mare’s behind in order to 
guess that she was a, that she was a, just the opposite of her, who was a 
lady in a palace or a pigsty, at the table or in bed, a lady of breeding, 
God-fearing, obeying His laws and submissive to His wishes, and with 
whom he could not perform, naturally, the acrobatics and trampish 

antics that he did with the other one, who, of course, was ready for 
anything, like the French matrons, and even worse, if one considers 
well, because they at least had the honesty to put a red light at their 
door, swinishness like that, just imagine, and that was all that was 
needed by the only and beloved daughter of Dona Renata Argote and 
Don Fernando del Carpio, and especially the latter, an upright man, a 
fine Christian, a Knight of the Order of the Holy Sepulcher, those who 
receive direct from God the privilege of remaining intact in their 
graves with their skin smooth like the cheeks of a bride and their eyes 
alive and clear like emeralds. 

Z See the graph “Growth in Genbank DNA Sequence Data” in chapter 

10 . 

Z Cheng Zhang and Jianpeng Ma, “Enhanced Sampling and 
Applications in Protein Folding in Explicit Solvent,” Journal of 
Chemical Physics 132, no. 24 (2010): 244101. See also about the Folding@home 
project, which has harnessed over five million computers around the 
world to simulate protein folding. 

4 For a more complete description of this argument, see the section 
“[The Impact...] on the Intelligent Destiny of the Cosmos: Why We 
Are Probably Alone in the Universe” in chapter 6 of The Singularity 
Is Near by Ray Kurzweil (New York: Viking, 2005). 

5, James D. Watson, Discovering the Brain (Washington, DC: National 
Academies Press, 1992). 

£L Sebastian Seung, Connectome: How the Brain’s Wiring Makes Us 
Who We Are (New York: Houghton Mifflin Harcourt, 2012). 

Z “Mandelbrot Zoom,” 
v=gEw8xpblaRA; “Fractal Zoom Mandelbrot Corner,” 

Chapter 1: Thought Experiments on the World 

L Charles Darwin, The Origin of Species (P. F. Collier & Son, 1909), 

Z Darwin, On the Origin of Species, 751 (206.1.1-6), Peckham’s 
Variorum edition, edited by Morse Peckham, The Origin of Species 
by Charles Darwin: A Variorum Text (Philadelphia: University of 
Pennsylvania Press, 1959). 

Z R. Dahm, “Discovering DNA: Friedrich Miescher and the Early 
Years of Nucleic Acid Research,” Human Genetics 122, no. 6 
(2008): 565-81, doi:10.1007/s00439-007-0433-0; PMID 17901982. 

4 Valery N. Soyfer, “The Consequences of Political Dictatorship for 
Russian Science,” Nature Reviews Genetics 2, no. 9 (2001): 723-29, 
doi: 10.1038/35088598; PMID 11533721. 

5, J. D. Watson and F. H. C. Crick, “A Structure for Deoxyribose 
Nucleic Acid,” Nature 171 (1953): 737-38, and “Double 
Helix: 50 Years of DNA,” Nature archive, 

(k Franklin died in 1958 and the Nobel Prize for the discovery of DNA 
was awarded in 1962. There is controversy as to whether or not she 
would have shared in that prize had she been alive in 1962. 

Z Albert Einstein, “On the Electrodynamics of Moving Bodies” 
(1905). This paper established the special theory of relativity. See 
Robert Bruce Lindsay and Henry Margenau, Foundations of Physics 
(Woodbridge, CT: Ox Bow Press, 1981), 330. 

8, “Crookes radiometer,” Wikipedia, 

9* Note that some of the momentum of the photons is transferred to the 
air molecules in the bulb (since it is not a perfect vacuum) and then 
transferred from the heated air molecules to the vane. 

10. Albert Einstein, “Does the Inertia of a Body Depend Upon Its 
Energy Content?” (1905). This paper established Einstein’s 
famous formula E = me 2 . 

11. “Albert Einstein’s Letters to President Franklin Delano Roosevelt,” 

Chapter 3: A Model of the Neocortex: The 
Pattern Recognition Theory of Mind 

E Some nonmammals, such as crows, parrots, and octopi, are reported 
to be capable of some level of reasoning; however, this is limited 
and has not been sufficient to create tools that have their own 
evolutionary course of development. These animals may have 
adapted other brain regions to perform a small number of levels of 
hierarchical thinking, but a neocortex is required for the relatively 
unrestricted hierarchical thinking that humans can perform. 

T V. B. Mountcastle, “An Organizing Principle for Cerebral Function: 
The Unit Model and the Distributed System” (1978), in Gerald M. 
Edelman and Vernon B. Mountcastle, The Mindful Brain: Cordcal 
Organization and the Group-Selective Theory of Higher Brain 
Function (Cambridge, MA: MIT Press, 1982). 

T Herbert A. Simon, “The Organization of Complex Systems,” in 
Howard H. Pattee, ed., Hierarchy Theory: The Challenge of 
Complex Systems (New York: George Braziller, Inc., 1973), 1973.pdf. 
4 Marc D. Hauser, Noam Chomsky, and W. Tecumseh Fitch, “The 
Faculty of Language: What Is It, Who Has It, and How Did It 
Evolve?” Science 298 (November 2002): 1569-79, 

5, The following passage from the book Transcend: Nine Steps to 
Living Well Forever, by Ray Kurzweil and Terry Grossman (New 
York: Rodale, 2009), describes this lucid dreaming technique in 
more detail: 

I’ve developed a method of solving problems while I sleep. I’ve 
perfected it for myself over several decades and have learned the 
subtle means by which this is likely to work better. 

I start out by assigning myself a problem when I get into bed. 
This can be any kind of problem. It could be a math problem, an issue 
with one of my inventions, a business strategy question, or even an 
interpersonal problem. 

I’ll think about the problem for a few minutes, but I try not to 

solve it. That would just cut off the creative problem solving to come. I 
do try to think about it. What do I know about this? What form could a 
solution take? And then I go to sleep. Doing this primes my 
subconscious mind to work on the problem. 

Terry: Sigmund Freud pointed out that when we dream, many of 
the censors in our brain are relaxed, so that we might dream about 
things that are socially, culturally, or even sexually taboo. We can 
dream about weird things that we wouldn’t allow ourselves to think 
about during the day. That’s at least one reason why dreams are 

Ray: There are also professional blinders that prevent people from 
thinking creatively, many of which come from our professional 
training, mental blocks such as “you can’t solve a signal processing 
problem that way” or “linguistics is not supposed to use those rules.” 
These mental assumptions are also relaxed in our dream state, so I’ll 
dream about new ways of solving problems without being burdened by 
these daytime constraints. 

Terry: There’s another part of our brain also not working when we 
dream, our rational faculties to evaluate whether an idea is reasonable. 
So that’s another reason that weird or fantastic things happen in our 
dreams. When the elephant walks through the wall, we aren’t shocked 
as to how the elephant could do this. We just say to our dream selves, 
“Okay, an elephant walked through the wall, no big deal.” Indeed, if I 
wake up in the middle of the night, I often find that I’ve been 
dreaming in strange and oblique ways about the problem that I 
assigned myself. 

Ray: The next step occurs in the morning in the halfway state 
between dreaming and being awake, which is often called lucid 
dreaming. In this state, I still have the feelings and imagery from my 
dreams, but now I do have my rational faculties. I realize, for example, 
that I am in a bed. And I could formulate the rational thought that I 
have a lot to do so I had better get out of bed. But that would be a 
mistake. Whenever I can, I will stay in bed and continue in this lucid 
dream state because that is key to this creative problem-solving 
method. By the way, this doesn’t work if the alarm rings. 

Reader: Sounds like the best of both worlds. 

Ray: Exactly. I still have access to the dream thoughts about the 
problem I assigned myself the night before. But now I’m sufficiently 
conscious and rational to evaluate the new creative ideas that came to 
me during the night. I can determine which ones make sense. After 
perhaps 20 minutes of this, I invariably will have keen new insights 
into the problem. 

I’ve come up with inventions this way (and spent the rest of the 
day writing a patent application), figured out how to organize material 
for a book such as this, and come up with useful ideas for a diverse set 
of problems. If I have a key decision to make, I will always go through 
this process, after which I am likely to have real confidence in my 

The key to the process is to let your mind go, to be nonjudgmental, 
and not to worry about how well the method is working. It is the 
opposite of a mental discipline. Think about the problem, but then let 
ideas wash over you as you fall asleep. Then in the morning, let your 
mind go again as you review the strange ideas that your dreams 
generated. I have found this to be an invaluable method for harnessing 
the natural creativity of my dreams. 

Reader: Well, for the workaholics among us, we can now work in 
our dreams. Not sure my spouse is going to appreciate this. 

Ray: Actually, you can think of it as getting your dreams to do 
your work for you. 

Chapter 4: The Biological Neocortex 

L Steven Pinker, How the Mind Works (New York: Norton, 1997), 

2, D. O. Hebb, The Organization of Behavior (New York: John Wiley 
& Sons, 1949). 

Z Henry Markram and Rodrigo Perrin, “Innate Neural Assemblies for 
Lego Memory,” Frontiers in Neural Circuits 5, no. 6 (2011). 

4 E-mail communication from Henry Markram, February 19, 2012. 

5, Van J. Wedeen et al., “The Geometric Structure of the Brain Fiber 
Pathways,” Science 335, no. 6076 (March 30, 2012). 

£L Tai Sing Lee, “Computations in the Early Visual Cortex,” Journal of 
Physiology—Paris 97 (2003): 121-39. 

Z A list of papers can be found at 

8, Daniel J. Felleman and David C. Van Essen, “Distributed 
Hierarchical Processing in the Primate Cerebral Cortex,” Cerebral 
Cortex 1, no. 1 (January/February 1991): 1-47. A compelling 
analysis of the Bayesian mathematics of the top-down and bottom- 
up communication in the neocortex is provided by Tai Sing Lee in 
“Hierarchical Bayesian Inference in the Visual Cortex,” Journal of 
the Optical Society of America 20, no. 7 (July 2003): 1434-48. 

9* Uri Hasson et al., “A Hierarchy of Temporal Receptive Windows in 
Human Cortex,” Journal of Neuroscience 28, no. 10 (March 5, 
2008): 2539-50. 

10. Marina Bedny et al., “Language Processing in the Occipital Cortex 

of Congenitally Blind Adults,” Proceedings of the National 
Academy of Sciences 108, no. 11 (March 15, 2011): 4429-34. 

11. Daniel E. Feldman, “Synaptic Mechanisms for Plasticity in 
Neocortex,” Annual Review of Neuroscience 32 (2009): 33-55. 

12. Aaron C. Koralek et al., “Corticostriatal Plasticity Is Necessary for 

Learning Intentional Neuroprosthetic Skills,” Nature 483 (March 
15, 2012): 331-35. 

13. E-mail communication from Randal Koene, January 2012. 

14. Min Fu, Xinzhu Yu, Ju Lu, and Yi Zuo, “Repetitive Motor 
Learning Induces Coordinated Formation of Clustered Dendritic 

Spines in Vivo,” Nature 483 (March 1, 2012): 92-95. 

15. Dario Bonanomi et al., “Ret Is a Multifunctional Coreceptor That 

Integrates Diffusible- and Contact-Axon Guidance Signals,” Cell 
148, no. 3 (February 2012): 568-82. 

16, See endnote 7 in chapter 11 . 

Chapter 5: The Old Brain 

L Vernon B. Mountcastle, “The View from Within: Pathways to the 
Study of Perception,” Johns Hopkins Medical Journal 136 (1975): 

Z B. Roska and F. Werblin, “Vertical Interactions Across Ten Parallel, 
Stacked Representations in the Mammalian Retina,” Nature 410, no. 
6828 (March 29, 2001): 583-87; “Eye Strips Images of All but Bare 
Essentials Before Sending Visual Information to Brain, UC 
Berkeley Research Shows,” University of California at Berkeley 
news release, March 28, 2001, 

3, Lloyd Watts, “Reverse-Engineering the Human Auditory Pathway,” 
in J. Liu et al., eds., WCCI 2012 (Berlin: Springer-Verlag, 2012), 
47-59. Lloyd Watts, “Real-Time, High-Resolution Simulation of the 
Auditory Pathway, with Application to Cell-Phone Noise 

Reduction,” ISCAS (June 2, 2010): 3821-24. For other papers see 

4 See Sandra Blakeslee, “Humanity? Maybe It’s All in the Wiring,” 
New York Times, December 11, 2003, 

5, T. E. J. Behrens et al., “Non-Invasive Mapping of Connections 
between Human Thalamus and Cortex Using Diffusion Imaging,” 
Nature Neuroscience 6, no. 7 (July 2003): 750-57. 

6, Timothy J. Buschman et al., “Neural Substrates of Cognitive 

Capacity Limitations,” Proceedings of the National Academy of 
Sciences 108, no. 27 (July 5, 2011): 11252-55, 

Z Theodore W. Berger et al., “A Cortical Neural Prosthesis for 
Restoring and Enhancing Memory,” Journal of Neural Enqineerinq 
8, no. 4 (August 2011). 

8, Basis functions are nonlinear functions that can be combined 
linearly (by adding together multiple weighted-basis functions) to 
approximate any nonlinear function. A. Pouget and L. H. Snyder, 
“Computational Approaches to Sensorimotor Transformations,” 
Nature Neuroscience 3, no. 11 Supplement (November 2000): 


9* J. R. Bloedel, “Functional Heterogeneity with Structural 
Homogeneity: How Does the Cerebellum Operate?” Behavioral and 
Brain Sciences 15, no. 4 (1992): 666-78. 

10. S. Grossberg and R. W. Paine, “A Neural Model of Cortico- 
Cerebellar Interactions during Attentive Imitation and Predictive 
Learning of Sequential Handwriting Movements,” Neural 
Networks 13, no. 8-9 (October-November 2000): 999-1046. 

11. Javier F. Medina and Michael D. Mauk, “Computer Simulation of 

Cerebellar Information Processing,” Nature Neuroscience 3 
(November 2000): 1205-11. 

12. James Olds, “Pleasure Centers in the Brain,” Scientific American 

(October 1956): 105-16. Aryeh Routtenberg, “The Reward 
System of the Brain,” Scientific American 239 (November 1978): 
154-64. K. C. Berridge and M. L. Kringelbach, “Affective 
Neuroscience of Pleasure: Reward in Humans and Other 
Animals,” Psychopharmacology 199 (2008): 457-80. Morten L. 
Kringelbach, The Pleasure Center: Trust Your Animal Instincts 
(New York: Oxford University Press, 2009). Michael R. 
Liebowitz, The Chemistry of Love (Boston: Little, Brown, 1983). 
W. L. Witters and P. Jones-Witters, Human Sexuality: A 
Biological Perspective (New York: Van Nostrand, 1980). 

Chapter 6: Transcendent Abilities 

L Michael Nielsen, Reinventing Discovery: The New Era of 
Networked Science (Princeton, NJ: Princeton University Press, 
2012), 1-3. T. Gowers and M. Nielsen, “Massively Collaborative 
Mathematics,” Nature 461, no. 7266 (2009): 879-81. “A 

Combinatorial Approach to Density Hales-Jewett,” Gowers’s 
Weblog, http://gowers.wordpress.eom/2009/02/01/a-combinatorial- 
approach-to-density-hales-jewett/. Michael Nielsen, “The Polymath 
Project: Scope of Participation,” March 20, 2009, Julie Rehmeyer, “SIAM: 
Massively Collaborative Mathematics,” Society for Industrial and 
Applied Mathematics, April 1, 2010, 

http ://www. .php?id=1731. 

T P. Dayan and Q. J. M. Huys, “Serotonin, Inhibition, and Negative 
Mood,” PLoS Computational Biology 4, no. 1 (2008), 

[CO ["vl pi 

Chapter 7: The Biologically Inspired Digital 

L Gary Cziko, Without Miracles: Universal Selection Theory and the 
Second Darwinian Revolution (Cambridge, MA: MIT Press, 1955). 

2, David Dalrymple has been a mentee of mine since he was eight 
years old in 1999. You can read his background here:, and 

T Jonathan Fildes, “Artificial Brain ‘10 Years Away,”’ BBC News, 
July 22, 2009, See also the 
video “Henry Markram on Simulating the Brain: The Next Decisive 

4 M. Mitchell Waldrop, “Computer Modelling: Brain in a Box,” 
Nature News, February 22, 2012, 

5, Jonah Lehrer, “Can a Thinking, Remembering, Decision-Making 
Biologically Accurate Brain Be Built from a Supercomputer?” Seed, 

Fildes, “Artificial Brain ‘10 Years Away.’” 


Anders Sandberg and Nick Bostrom, Whole Brain Emulation: A 
Roadmap, Technical Report #2008-3 (2008), Future of Humanity 
Institute, Oxford University, 

9* Here is the basic schema for a neural net algorithm. Many variations 
are possible, and the designer of the system needs to provide certain 
critical parameters and methods, detailed on the following pages. 

Creating a neural net solution to a problem involves the following 

Define the input. 

Define the topology of the neural net (i.e., the layers of neurons 

and the connections between the neurons). 

Train the neural net on examples of the problem. 

Run the trained neural net to solve new examples of the problem. 
Take your neural net company public. 

These steps (except for the last one) are detailed below: 

The Problem Input 

The problem input to the neural net consists of a series of numbers. 
This input can be: 

In a visual pattern recognition system, a two-dimensional array of 
numbers representing the pixels of an image; or 

In an auditory (e.g., speech) recognition system, a two-dimensional 
array of numbers representing a sound, in which the first dimension 
represents parameters of the sound (e.g., frequency components) and 
the second dimension represents different points in time; or 

In an arbitrary pattern recognition system, an n-dimensional array 
of numbers representing the input pattern. 

Defining the Topology 

To set up the neural net, the architecture of each neuron consists of: 

Multiple inputs in which each input is “connected” to either the 
output of another neuron or one of the input numbers. 

Generally, a single output, which is connected to either the input of 
another neuron (which is usually in a higher layer) or the final output. 

Set Up the First Layer of Neurons 

Create N 0 neurons in the first layer. For each of these neurons, 
“connect” each of the multiple inputs of the neuron to “points” (i.e., 
numbers) in the problem input. These connections can be determined 
randomly or using an evolutionary algorithm (see below). 

Assign an initial “synaptic strength” to each connection created. 
These weights can start out all the same, can be assigned randomly, or 
can be determined in another way (see below). 

Set Up the Additional Layers of Neurons 

Set up a total of M layers of neurons. For each layer, set up the neurons 
in that layer. 

For layerp 

Create N [ neurons in layer^ For each of these neurons, “connect” 
each of the multiple inputs of the neuron to the outputs of the neurons 
in layer,., (see variations below). 

Assign an initial “synaptic strength” to each connection created. 
These weights can start out all the same, can be assigned randomly, or 
can be determined in another way (see below). 

The outputs of the neurons in layer M are the outputs of the neural 
net (see variations below). 

The Recognition Trials 
How Each Neuron Works 

Once the neuron is set up, it does the following for each 
recognition trial: 

Each weighted input to the neuron is computed by multiplying the 
output of the other neuron (or initial input) that the input to this neuron 
is connected to by the synaptic strength of that connection. 

All of these weighted inputs to the neuron are summed. 

If this sum is greater than the firing threshold of this neuron, then 
this neuron is considered to fire and its output is 1. Otherwise, its 
output is 0 (see variations below). 

Do the Following for Each Recognition Trial 

For each layer, from layer 0 to layer M : 

For each neuron in the layer: 

Sum its weighted inputs (each weighted input = the output of the 
other neuron [or initial input] that the input to this neuron is connected 
to, multiplied by the synaptic strength of that connection). 

If this sum of weighted inputs is greater than the firing threshold 
for this neuron, set the output of this neuron = 1, otherwise set it to 0. 

To Train the Neural Net 

Run repeated recognition trials on sample problems. 

After each trial, adjust the synaptic strengths of all the interneuronal 
connections to improve the performance of the neural net on this trial (see 
the discussion below on how to do this). 

Continue this training until the accuracy rate of the neural net is no 
longer improving (i.e., reaches an asymptote). 

Key Design Decisions 

In the simple schema above, the designer of this neural net algorithm needs 
to determine at the outset: 

What the input numbers represent. 

The number of layers of neurons. 

The number of neurons in each layer. (Each layer does not necessarily 
need to have the same number of neurons.) 

The number of inputs to each neuron in each layer. The number of 
inputs (i.e., interneuronal connections) can also vary from neuron to neuron 
and from layer to layer. 

The actual “wiring” (i.e., the connections). For each neuron in each 
layer, this consists of a list of other neurons, the outputs of which constitute 
the inputs to this neuron. This represents a key design area. There are a 
number of possible ways to do this: 

(1) Wire the neural net randomly; or 

(2) Use an evolutionary algorithm (see below) to determine an optimal 

wiring; or 

(3) Use the system designer’s best judgment in determining the wiring. 

The initial synaptic strengths (i.e., weights) of each connection. There 
are a number of possible ways to do this: 

(1) Set the synaptic strengths to the same value; or 

(2) Set the synaptic strengths to different random values; or 

(3) Use an evolutionary algorithm to determine an optimal set of initial 

values; or 

(4) Use the system designer’s best judgment in determining the initial 


The firing threshold of each neuron. 

Determine the output. The output can be: 

(1) the outputs of layer M of neurons; or 

(2) the output of a single output neuron, the inputs of which are the outputs 

of the neurons in layer M ; or 

(3) a function of (e.g., a sum of) the outputs of the neurons in layer M ; or 

(4) another function of neuron outputs in multiple layers. 

Determine how the synaptic strengths of all the connections are adjusted 
during the training of this neural net. This is a key design decision and is 
the subject of a great deal of research and discussion. There are a number of 
possible ways to do this: 

(1) For each recognition trial, increment or decrement each synaptic 
strength by a (generally small) fixed amount so that the neural net’s 
output more closely matches the correct answer. One way to do this is 
to try both incrementing and decrementing and see which has the more 
desirable effect. This can be time-consuming, so other methods exist 
for making local decisions on whether to increment or decrement each 
synaptic strength. 

(2) Other statistical methods exist for modifying the synaptic strengths after 
each recognition trial so that the performance of the neural net on that 
trial more closely matches the correct answer. 

Note that neural net training will work even if the answers to the 
training trials are not all correct. This allows using real-world training data 
that may have an inherent error rate. One key to the success of a neural net- 
based recognition system is the amount of data used for training. Usually a 
very substantial amount is needed to obtain satisfactory results. As with 
human students, the amount of time that a neural net spends learning its 
lessons is a key factor in its performance. 


Many variations of the above are feasible. For example: 

There are different ways of determining the topology. In particular, the 
interneuronal wiring can be set either randomly or using an evolutionary 

There are different ways of setting the initial synaptic strengths. 

The inputs to the neurons in laye^ do not necessarily need to come from 
the outputs of the neurons in layer,.]. Alternatively, the inputs to the 
neurons in each layer can come from any lower layer or any layer. 

There are different ways to determine the final output. 

The method described above results in an “all or nothing” (1 or 0) firing 
called a nonlinearity. There are other nonlinear functions that can be used. 
Commonly a function is used that goes from 0 to 1 in a rapid but more 
gradual fashion. Also, the outputs can be numbers other than 0 and 1. 

The different methods for adjusting the synaptic strengths during 
training represent key design decisions. 

The above schema describes a “synchronous” neural net, in which each 
recognition trial proceeds by computing the outputs of each layer, starting 
with layer 0 through layer M . In a true parallel system, in which each neuron 

is operating independently of the others, the neurons can operate 
“asynchronously” (i.e., independently). In an asynchronous approach, each 
neuron is constantly scanning its inputs and fires whenever the sum of its 
weighted inputs exceeds its threshold (or whatever its output function 

10. Robert Mannell, “Acoustic Representations of Speech,” 2008, 

11. Here is the basic schema for a genetic (evolutionary) algorithm. Many 

variations are possible, and the designer of the system needs to provide 
certain critical parameters and methods, detailed below. 

The Evolutionary Algorithm 

Create N solution “creatures.” Each one has: 

A genetic code: a sequence of numbers that characterize a possible 
solution to the problem. The numbers can represent critical parameters, 
steps to a solution, rules, etc. 

For each generation of evolution, do the following: 

Do the following for each of the N solution creatures: 

Apply this solution creature’s solution (as represented by its genetic 
code) to the problem, or simulated environment. Rate the solution. 

Pick the L solution creatures with the highest ratings to survive into the 
next generation. 

Eliminate the (N - L) nonsurviving solution creatures. 

Create (N - L ) new solution creatures from the L surviving solution 
creatures by: 

(1) Making copies of the L surviving creatures. Introduce small random 

variations into each copy; or 

(2) Create additional solution creatures by combining parts of the genetic 

code (using “sexual” reproduction, or otherwise combining portions of 
the chromosomes) from the L surviving creatures; or 

(3) Do a combination of (1) and (2). 

Determine whether or not to continue evolving: 

Improvement = (highest rating in this generation) - (highest rating in 
the previous generation). 

If Improvement < Improvement Threshold then we’re done. 

The solution creature with the highest rating from the last generation of 

evolution has the best solution. Apply the solution defined by its genetic 
code to the problem. 

Key Design Decisions 

In the simple schema above, the designer needs to determine at the outset: 
Key parameters: 



Improvement threshold. 

What the numbers in the genetic code represent and how the solution is 
computed from the genetic code. 

A method for determining the N solution creatures in the first 
generation. In general, these need only be “reasonable” attempts at a 
solution. If these first-generation solutions are too far afield, the 
evolutionary algorithm may have difficulty converging on a good solution. 
It is often worthwhile to create the initial solution creatures in such a way 
that they are reasonably diverse. This will help prevent the evolutionary 
process from just finding a “locally” optimal solution. 

How the solutions are rated. 

How the surviving solution creatures reproduce. 


Many variations of the above are feasible. For example: 

There does not need to be a fixed number of surviving solution 
creatures (L) from each generation. The survival rule(s) can allow for a 
variable number of survivors. 

There does not need to be a fixed number of new solution creatures 
created in each generation (N - L). The procreation rules can be 
independent of the size of the population. Procreation can be related to 
survival, thereby allowing the fittest solution creatures to procreate the 

The decision as to whether or not to continue evolving can be varied. It 
can consider more than just the highest-rated solution creature from the 
most recent generation(s). It can also consider a trend that goes beyond just 
the last two generations. 

12. Dileep George, “How the Brain Might Work: A Hierarchical and 
Temporal Model for Learning and Recognition” (PhD dissertation, 
Stanford University, June 2008). 

13. A. M. Turing, “Computing Machinery and Intelligence,” Mind, October 


14. Hugh Loebner has a “Loebner Prize” competition that is run each year. 

The Loebner silver medal will go to a computer that passes Turing’s 
original text-only test. The gold medal will go to a computer that can 
pass a version of the test that includes audio and video input and 
output. In my view, the inclusion of audio and video does not actually 
make the test more challenging. 

15. “Cognitive Assistant That Learns and Organizes,” Artificial Intelligence 

Center, SRI International, 

16. Dragon Go! Nuance Communications, Inc., 

17. “Overcoming Artificial Stupidity,” WolframAlpha Blog, April 17, 2012, 

Chapter 8: The Mind as Computer 

L Salomon Bochner, A Biographical Memoir of John von Neumann 
(Washington, DC: National Academy of Sciences, 1958). 

Z A. M. Turing, “On Computable Numbers, with an Application to the 
Entscheidungsproblem,” Proceedings of the London Mathematical 
Society Series 2, vol. 42 (1936-37): 230-65, 
ie.pdf. A. M. Turing, “On Computable Numbers, with an 
Application to the Entscheidungsproblem: A Correction,” 
Proceedings of the London Mathematical Society 43 (1938): 544- 

Z John von Neumann, “First Draft of a Report on the ED VAC,” 
Moore School of Electrical Engineering, University of 
Pennsylvania, June 30, 1945. John von Neumann, “A Mathematical 
Theory of Communication,” Bell System Technical Journal, July 
and October 1948. 

4 Jeremy Bernstein, The Analytical Engine: Computers — Past, 
Present, and Future, rev. ed. (New York: William Morrow & Co., 

5, “Japan’s K Computer Tops 10 Petaflop/s to Stay Atop TOP500 
List,” Top 500, November 11, 2011, 

£L Carver Mead, Analog VLSI and Neural Systems (Reading, MA: 
Addison-Wesley, 1986). 

Z “IBM Unveils Cognitive Computing Chips,” IBM news release, 
August 18, 2011, http://www- 

8, “Japan’s K Computer Tops 10 Petaflop/s to Stay Atop TOP500 

Chapter 9: Thought Experiments on the Mind 

L John R. Searle, “I Married a Computer,” in Jay W. Richards, ed., 
Are We Spiritual Machines? Ray Kurzweil vs. the Critics of Strong 
AI (Seattle: Discovery Institute, 2002). 

Z Stuart Hameroff, Ultimate Computing: Biomolecular Consciousness 
and Nanotechnology (Amsterdam: Elsevier Science, 1987). 

Z R S. Sebel et al., “The Incidence of Awareness during Anesthesia: A 
Multicenter United States Study,” Anesthesia and Analgesia 99 
(2004): 833-39. 

4 Stuart Sutherland, The International Dictionary of Psychology (New 
York: Macmillan, 1990). 

5, David Cockburn, “Human Beings and Giant Squids,” Philosophy 
69, no. 268 (April 1994): 135-50. 

(k Ivan Petrovich Pavlov, from a lecture given in 1913, published in 
Lectures on Conditioned Reflexes: Twenty-Five Years of Objective 
Study of the Higher Nervous Activity [Behavior] of Animals 
(London: Martin Lawrence, 1928), 222. 

Z Roger W. Sperry, from James Arthur Lecture on the Evolution of the 
Human Brain, 1964, p. 2. 

8, Henry Maudsley, “The Double Brain,” Mind 14, no. 54 (1889): 

9* Susan Curtiss and Stella de Bode, “Language after 
Hemispherectomy,” Brain and Cognition 43, nos. 1-3 (June-August 
2000): 135-38. 

10. E. P. Vining et al., “Why Would You Remove Half a Brain? The 

Outcome of 58 Children after Hemispherectomy—the Johns 
Hopkins Experience: 1968 to 1996,” Pediatrics 100 (August 
1997): 163-71. M. B. Pulsifer et al., “The Cognitive Outcome of 
Hemispherectomy in 71 Children,” Epilepsia 45, no. 3 (March 
2004): 243-54. 

11. S. McClelland III and R. E. Maxwell, “Hemispherectomy for 
Intractable Epilepsy in Adults: The First Reported Series,” Annals 
of Neurology 61, no. 4 (April 2007): 372-76. 

12. Lars Muckli, Marcus J. Naumerd, and Wolf Singer, “Bilateral 
Visual Field Maps in a Patient with Only One Hemisphere,” 

Proceedings of the National Academy of Sciences 106, no. 31 
(August 4, 2009), 

13. Marvin Minsky, The Society of Mind (New York: Simon and 
Schuster, 1988). 

14. F. Fay Evans-Martin, The Nervous System (New York: Chelsea 
House, 2005), 

15. Benjamin Libet, Mind Time: The Temporal Factor in 
Consciousness (Cambridge, MA: Harvard University Press, 

16. Daniel C. Dennett, Freedom Evolves (New York: Viking, 2003). 

17. Michael S. Gazzaniga, Who’s in Charge? Free Will and the Science 

of the Brain (New York: Ecco/HarperCollins, 2011). 

18. David Hume, An Enquiry Concerning Human Understanding 
(1765), 2nd ed., edited by Eric Steinberg (Indianapolis: Hackett, 

19. Arthur Schopenhauer, The Wisdom of Life. 

20. Arthur Schopenhauer, On the Freedom of the Will (1839). 

21. From Raymond Smullyan, 5000 B.C. and Other Philosophical 
Fantasies (New York: St. Martin’s Press, 1983). 

22. For an insightful and entertaining examination of similar issues of 

identity and consciousness, see Martine Rothblatt, “The Terasem 
Mind Uploading Experiment,” International Journal of Machine 
Consciousness 4, no. 1 (2012): 141-58. In this paper, Rothblatt 
examines the issue of identity with regard to software that 
emulates a person based on “a database of video interviews and 
associated information about a predecessor person.” In this 
proposed future experiment, the software is successfully 
emulating the person it is based on. 

23. “How Do You Persist When Your Molecules Don’t?” Science and 

Consciousness Review 1, no. 1 (June 2004), http://www.sci- 


Chapter 10: The Law of Accelerating Returns 
Applied to the Brain 

L “DNA Sequencing Costs,” National Human Genome Research 
Institute, NIH, 

2, “Genetic Sequence Data Bank, Distribution Release Notes,” 
December 15, 2009, National Center for Biotechnology 

Information, National Library of Medicine, 

T “DNA Sequencing—The History of DNA Sequencing,” January 2, 

4 “Cooper’s Law,” ArrayComm, 

5, “The Zettabyte Era,” Cisco, 
and “Number of Internet Hosts,” Internet Systems Consortium, 

TeleGeography © PriMetrica, Inc., 2012. 

Dave Kristula, “The History of the Internet” (March 1997, update 
August 2001), 
history.shtml; Robert Zakon, “Hobbes’ Internet Timeline v8.0,”; Quest 

Communications, 8-K for 9/13/1998 EX-99.1; Converge! Network 
Digest, December 5, 2002, 

http ://www. convergedige asp? 
vn=v9n229&fecha=December%2005,%202002; Jim Duffy, “AT&T 
Plans Backbone Upgrade to 40G,” Computerworld, June 7, 2006, 
command=viewArticleBasic&articleId=9001032; “40G: The Fastest 
Connection You Can Get?”, November 2, 2007, 
http ://www. internetnews. com/inf r a/article .php/3708936; “Verizon 

First Global Service Provider to Deploy 100G on U.S. Long-Haul 
Network,” news release, Verizon, 

8, Facebook, “Key Facts,” 

http ://newsroom. fb. com/content/def ault. aspx?Ne ws Areald=2 2. 


10 , Calculations per Second per $1,000 


Calculations per 
Second per $1,000 


Natural Logarithm 



Analytical Engine 




Hollerith Tabulator 




Monroe Calculator 




IBM Tabulator 




National Ellis 3000 








Bell Calculator Model 1 




Zuse 3 
























Univac I 




Univac 1103 




IBM 701 












IBM 704 




Datamatic 1000 

























Univac II 



IBM 1620 









Univac III 






IBM 1130 






IBM 360 Model 75 









Data General Nova 



Altair 8800 



DEC PDP-11 Model 70 



Cray 1 



Apple II 



DEC VAX 11 Model 780 









Compaq Portable 



IBM AT-80286 



Apple Macintosh 




Compaq Deskpro 386 




Apple Mac 11 




Pentium PC 



4.81 E+07 

Pentium PC 




Pentium 11 PC 




Pentium III PC 








Power Macintosh G4/500 




Power Macintosh G5 2.0 




Dell Dimension 8400 




Power Mac G5 Quad 




Dell XPS 630 




Mac Pro 




Intel Core i7 Desktop 




Intel Core i7 Desktop 


11. Top 500 Supercomputer Sites, 

12. “Microprocessor Quick Reference Guide,” Intel Research, 

IT 1971-2000: VLSI Research Inc. 

2001-2006: The International Technology Roadmap for 

Semiconductors, 2002 Update and 2004 Update, Table 7a, “Cost— 
Near-term Years,” “DRAM cost/bit at (packaged microcents) at 

2007-2008: The International Technology Roadmap for 

Semiconductors, 2007, Tables 7a and 7b, “Cost—Near-term Years,” 
“Cost—Long-term Years,” 

2009-2022: The International Technology Roadmap for 

Semiconductors, 2009, Tables 7a and 7b, “Cost—Near-term Years,” 
“Cost—Long-term Years,” 

14, To make all dollar values comparable, computer prices for all years 
were converted to their year 2000 dollar equivalent using the 
Federal Reserve Board’s CPI data at For example, $1 

million in 1960 is equivalent to $5.8 million in 2000, and $1 
million in 2004 is equivalent to $0.91 million in 2000. 


1951: Richard E. Matick, Computer Storage Systems and 
Technology (New York: John Wiley & Sons, 1977); 

1955: Matick, Computer Storage Systems and Technology; 

OECD, 1968, 





1964: Matick, Computer Storage Systems and Technology;; 
http ://www. ddj. com/documents/s=1493/ddj 0005hc/. 

1965: Matick, Computer Storage Systems and Technology; 

http ://www. f ourmilab. ch/documents/univac/conf ig 1108 .xhtml; 
http ://www. f robenius. com/univac .htm. 

1968: Data General. 

1969, 1970: 

1974: Scientific Electronic Biological Computer Consulting 

1975-1996: Byte magazine advertisements. 

1997-2000: PC Computing magazine advertisements. 


news/price watch/raw/pw-020624). 


http ://sharkyextreme. com/guide s/WMPG/article .php/10706_222719 1_\ 
2004: (11/17/04). 

2008: (10/02/08) ($16.61). 

15. Dataquest/Intel and Pathfinder Research: 

Year $ Log ($) 

1968 1.00000000 


1969 0.85000000 


1970 0.60000000 


1971 0.30000000 


1972 0.15000000 


1973 0.10000000 


1974 0.07000000 


1975 0.02800000 


1976 0.01500000 


1977 0.00800000 


1978 0.00500000 


1979 0.00200000 


1980 0.00130000 


1981 0.00082000 


1982 0.00040000 


1983 0.00032000 


1984 0.00032000 


1985 0.00015000 


1986 0.00009000 


1987 0.00008100 


1988 0.00006000 


1989 0.00003500 


1990 0.00002000 


1991 0.00001700 


1992 0.00001000 


1993 0.00000900 


1994 0.00000800 


1995 0.00000700 


1996 0.00000500 


1997 0.00000300 


1998 0.00000140 


1999 0.00000095 


2000 0.00000080 


2001 0.00000035 -14.8653 

2002 0.00000026 -15.1626 

2003 0.00000017 -15.5875 

2004 0.00000012 -15.9358 

2005 0.000000081 -16.3288 

2006 0.000000063 -16.5801 

2007 0.000000024 -17.5452 

2008 0.000000016 -17.9507 

16. Steve Cullen, In-Stat, September 2008, 
Year Mbits Bits 

1971921.6 9.216E+08 

1972 3788.8 3.789E+09 

1973 8294.4 8.294E+09 

197419865.6 1.987E+10 

1975 42700.8 4.270E+10 

1976130662.4 1.307E+11 

1977 276070.4 2.761E+11 

1978 663859.2 6.639E+11 

19791438720.0 1.439E+12 

1980 3172761.6 3.173E+12 

19814512665.6 4.513E+12 

198211520409.6 1.152E+13 

1983 29648486.4 2.965E+13 

1984 68418764.8 6.842E+13 

1985 87518412.8 8.752E+13 

1986 192407142.4 1.924E+14 

1987 255608422.4 2.556E+14 

1988 429404979.2 4.294E+14 

1989 631957094.4 6.320E+14 

1990 950593126.4 9.506E+14 

1991 1546590618 1.547E+15 

1992 2845638656 2.846E+15 

1993 4177959322 4.178E+15 

1994 7510805709 7.511E+15 

1995 13010599936 1.301E+16 

1996 23359078007 2.336E+16 

1997 45653879161 4.565E+16 

1998 85176878105 8.518E+16 

1999 1.47327E+11 1.473E+17 

2000 2.63636E+11 2.636E+17 

2001 4.19672E+11 4.197E+17 

2002 5.90009E+11 5.900E+17 

2003 8.23015E+11 8.230E+17 

2004 1.32133E+12 1.321E+18 

2005 1.9946E+12 1.995E+18 

2006 2.94507E+12 2.945E+18 

2007 5.62814E+12 5.628E+18 

17. “Historical Notes about the Cost of Hard Drive Storage Space,”; Byte 
magazine advertisements, 1977-1998; PC Computing magazine 
advertisements, 3/1999; Understanding Computers: Memory and 
Storage (New York: Time Life, 1990);; John C. 
McCallum, “Disk Drive Prices (1955-2012),”; IBM, “Frequently Asked 
Questions,” http://www-; IBM, “IBM 355 
Disk Storage Unit,” http://www-; 

IBM, “IBM 3380 Direct Access Storage Device,” http://www.03- 

18. “Without Driver or Map, Vans Go from Italy to China,” Sydney 

Morning Herald, October 29, 2010, 


20. Adapted with permission from Amiram Grinvald and Rina 

Hildesheim, “VSDI: A New Era in Functional Imaging of 

Cortical Dynamics,” Nature Reviews Neuroscience 5 (November 

2004): 874-85. 

The main tools for imaging the brain are shown in this diagram. 
Their capabilities are depicted by the shaded rectangles. 

Spatial resolution refers to the smallest dimension that can be 
measured with a technique. Temporal resolution is imaging time or 
duration. There are tradeoffs with each technique. For example, EEG 
(electroencephalography), which measures “brain waves” (electrical 
signals from neurons), can measure very rapid brain waves (occurring 
in short time intervals), but can only sense signals near the surface of 
the brain. 

In contrast, fMRI (functional magnetic resonance imaging), 
which uses a special MRI machine to measure blood flow to neurons 
(indicating neuron activity), can sense a lot deeper in the brain (and 
spinal cord) and with higher resolution, down to tens of microns 
(millionths of a meter). However, fMRI operates very slowly 
compared with EEG. 

These are noninvasive techniques (no surgery or drugs are 
required). MEG (magnetoencephalography) is another noninvasive 
technique. It detects magnetic fields generated by neurons. MEG and 
EEG can resolve events with a temporal resolution of down to 1 
millisecond, but better than fMRI, which can at best resolve events 
with a resolution of several hundred milliseconds. MEG also 
accurately pinpoints sources in primary auditory, somatosensory, and 
motor areas. 

Optical imaging covers almost the entire range of spatial and 
temporal resolutions, but is invasive. VSDI (voltage-sensitive dyes) is 
the most sensitive method of measuring brain activity, but is limited to 
measurements near the surface of the cortex of animals. 

The exposed cortex is covered with a transparent sealed chamber; 
after the cortex is stained with a suitable voltage-sensitive dye, it is 
illuminated with light and a sequence of images is taken with a high¬ 
speed camera. Other optical techniques used in the lab include ion 
imaging (typically calcium or sodium ions) and fluorescence imaging 
systems (confocal imaging and multiphoton imaging). 

Other lab techniques include PET (positron emission tomography, 

a nuclear medicine imaging technique that produces a 3-D image), 
2DG (2-deoxyglucose postmortem histology, or tissue analysis), 
lesions (involves damaging neurons in an animal and observing the 
effects), patch clamping (to measure ion currents across biological 
membranes), and electron microscopy (using an electron beam to 
examine tissues or cells at a very fine scale). These techniques can also 
be integrated with optical imaging. 

21. MRI spatial resolution in microns (pm), 1980-2012: 


Resolution in Citation 




125 "Characterization of 

Cerebral White Matter 
Properties Using 
Quantitative Magnetic 
Resonance Imaging Stains' 


200 “Study of Brain Anatomy 

with High-Field MRI: 

Recent Progress” 





250 “High-Resolution Phased- 

Array MRI of the Human 
Brain at 7 Tesla: Initial 
Experience in Multiple 
Sclerosis Patients" 

/), 1552-6569.2008.00338.X 


1,000 “Mapping Human Brain 

Activity in Vivo” 



1.700 “Neurolmaging in Patients 

with Seizures of Probable 
Frontal Lobe Origin” 


org/10.111 l/j.1528-1157 



1,700 “A Study of the Septum 

Pellucidum and Corpus 
Callosum in Schizophrenia 
with MR Imaging” 


.org/10.1111/J. 1600-0447 



1.700 “Clinical Efficiency of 

Nuclear Magnetic Reso¬ 
nance Imaging" 



5,000 “In Vivo NMR Imaging in 

Medicine: The Aberdeen 
Approach, Both Physical 
and Biological [and 

http://dx doi.orgT0.l098 
/rstb 1980.0071 

22. Spatial resolution in nanometers (nm) of destructive imaging 
techniques, 1983-2011: 










x-y re s 













'Focused Ion Beam 

Millingand Scanning 
Electron Microscopy of 
Brain Tissue" 



Focused ion beam/ 
scanning electron 
microscope (T1B/SEM) 

"Volume Electron 
Microscopy for Neuronal 
Circuit Reconstruction" 
/).cori>.20ll 10,022 

Scanning electron 
m k roscopy (S E M) 

’’Volume Electron 
Microscopy for Neuronal 

Ci rc u it Reconst rue lion" 
/).conb. 2011.10.022 

Transmission electron 
microscopy (TEM) 

"Scnal Block- Face Scan¬ 
ning Electron Microscopy 
to Reconstruct Threc- 
I hmcnsKUM 1T issue 


hi tp: ltd 
/tour na 1 pbkv0020329 

Serial hlock-face scanning 
electron microscopy 

Result quoted in http.// 
faculty vs. t a mu edu/choe 
/It p'pu hlicat ions/cho c 
provided by Yoonsuck 

“Wet SUM: A Novd 

Method for Rapid 

Diagnosis of Brain 


http ://dx dot 



"Wet" scanning elect ton 
mi cToscopy (wet SE M) 

“A Depolarizing Chloride 
Current Contributes to 
Chcmoeftect rival 
Transduction in 

Olfactory Sensory 

Neurons in Situ* 

ht tp: tt w w w.jncu rose i 


Scanning transmtision 
electron microscope 

'Enhanced Optical Imag¬ 
ing of Rat Ghoinas and 
Tumor Margins" 

ht tp. //journ al v hvw 
.com/neu rosu rgcry 
/ Enh an ced.Optical 
_1 roag>ng^of_Rat_( ilK*mas 
_and_Tumor. I9.a%px 

Enhanced optical imaging 

With a spatial resolution 
of the optical images 
below 20 microns 2'pixel 

"3D Imaging of X-Ray 



Projection microscopy 

Sec Fig 7 in article. 

23. Spatial resolution in microns (pm) of nondestructive imaging 
techniques in animals, 1985-2012: 

Year Finding 

2012 Resolution 0.07 




Sebastian Berning et al., “Nanoscopy in a Living Mouse 
Brain,” Science 335, no. 6068 (February 3, 2012): 551. 
Stimulated emission depletion (STED) fluorescence 


2012 Resolution 

2004 Resolution 





1996 Resolution 





1995 Resolution 




Highest resolution achieved in vivo so far 

Sebastian Berning et al., “Nanoscopy in a Living Mouse 
Brain,” Science 335, no. 6068 (February 3, 2012): 551. 
Confocal and multiphoton microscopy 

Amiram Grinvald and Rina Hildesheim, “VSDI: A New 
Era in Functional Imaging of Cortical Dynamics,” Nature 
Reviews Neuroscience 5 (November 2004): 874-85. 
Imaging based on voltage-sensitive dyes (VSDI) 

“VSDI has provided high-resolution maps, which 
correspond to cortical columns in which spiking occurs, 
and offer a spatial resolution better than 50 pm.” 


Dov Malonek and Amiram Grinvald, “Interactions 
between Electrical Activity and Cortical Microcirculation 
Revealed by Imaging Spectroscopy: Implications for 
Functional Brain Mapping,” Science 272, no. 5261 (April 
26, 1996): 551-54. 
Imaging spectroscopy 

“The study of spatial relationships between individual 
cortical columns within a given brain area has become 
feasible with optical imaging based on intrinsic signals, at 
a spatial resolution of about 50 pm.” 


D. H. Turnbull et al., “Ultrasound Backscatter Microscope 
Analysis of Early Mouse Embryonic Brain 
Development,” Proceedings of the National Academy of 
Sciences 92, no. 6 (March 14, 1995): 2239-43. 
http ://www.pnas. org/content/92/6/2239. short 
Ultrasound backscatter microscopy 
“We demonstrate application of a real-time imaging 

method called ultrasound backscatter microscopy for 
Notes visualizing mouse early embryonic neural tubes and 
hearts. This method was used to study live embryos in 
utero between 9.5 and 11.5 days of embryogenesis, with a 
spatial resolution close to 50 pm.” 

1985 Resolution 500 

H. S. Orbach, L. B. Cohen, and A. Grinvald, “Optical 
Citation Mapping of Electrical Activity in Rat Somatosensory and 
Visual Cortex,” Journal of Neuroscience 5, no. 7 (July 1, 
1985): 1886-95. 

URL http ://www. j neurosci. org/content/5/7/1886. short 

Technique Optical methods 

Chapter 11: Objections 

L Paul G. Allen and Mark Greaves, “Paul Allen: The Singularity Isn’t 
Near,” Technology Review, October 12, 2011, 

Z ITRS, “International Technology Roadmap for Semiconductors,” 

T Ray Kurzweil, The Singularity Is Near (New York: Viking, 2005), 
chapter 2. 

4 Endnote 2 in Allen and Greaves, “The Singularity Isn’t Near,” reads 
as follows: “We are beginning to get within range of the computer 
power we might need to support this kind of massive brain 
simulation. Petaflop-class computers (such as IBM’s BlueGene/P 
that was used in the Watson system) are now available 
commercially. Exaflop-class computers are currently on the drawing 
boards. These systems could probably deploy the raw computational 
capability needed to simulate the firing patterns for all of a brain’s 
neurons, though currently it happens many times more slowly than 
would happen in an actual brain.” 

5, Kurzweil, The Singularity Is Near, chapter 9, section titled “The 
Criticism from Software” (pp. 435-42). 

£L Ibid., chapter 9. 

Z Although it is not possible to precisely determine the information 
content in the genome, because of the repeated base pairs it is 
clearly much less than the total uncompressed data. Here are two 
approaches to estimating the compressed information content of the 
genome, both of which demonstrate that a range of 30 to 100 
million bytes is conservatively high. 

1. In terms of the uncompressed data, there are 3 billion DNA 
rungs in the human genetic code, each coding 2 bits (since there are 
four possibilities for each DNA base pair). Thus the human genome is 
about 800 million bytes uncompressed. The noncoding DNA used to 
be called “junk DNA,” but it is now clear that it plays an important 
role in gene expression. However, it is very inefficiently coded. For 
one thing, there are massive redundancies (for example, the sequence 
called “ALU” is repeated hundreds of thousands of times), which 

compression algorithms can take advantage of. 

With the recent explosion of genetic data banks, there is a great 
deal of interest in compressing genetic data. Recent work on applying 
standard data compression algorithms to genetic data indicates that 
reducing the data by 90 percent (for bit perfect compression) is 
feasible: Hisahiko Sato et al., “DNA Data Compression in the Post 
Genome Era,” Genome Informatics 12 (2001): 512-14, 

Thus we can compress the genome to about 80 million bytes 
without loss of information (meaning we can perfectly reconstruct the 
full 800-million-byte uncompressed genome). 

Now consider that more than 98 percent of the genome does not 
code for proteins. Even after standard data compression (which 
eliminates redundancies and uses a dictionary lookup for common 
sequences), the algorithmic content of the noncoding regions appears 
to be rather low, meaning that it is likely that we could code an 
algorithm that would perform the same function with fewer bits. 
However, since we are still early in the process of reverse-engineering 
the genome, we cannot make a reliable estimate of this further 
decrease based on a functionally equivalent algorithm. I am using, 
therefore, a range of 30 to 100 million bytes of compressed 
information in the genome. The top part of this range assumes only 
data compression and no algorithmic simplification. 

Only a portion (although the majority) of this information 
characterizes the design of the brain. 

2. Another line of reasoning is as follows. Though the human 
genome contains around 3 billion bases, only a small percentage, as 
mentioned above, codes for proteins. By current estimates, there are 
26,000 genes that code for proteins. If we assume those genes average 
3,000 bases of useful data, those equal only approximately 78 million 
bases. A base of DNA requires only 2 bits, which translate to about 20 
million bytes (78 million bases divided by four). In the protein-coding 
sequence of a gene, each “word” (codon) of three DNA bases 
translates into one amino acid. There are, therefore, 4 3 (64) possible 
codon codes, each consisting of three DNA bases. There are, however, 
only 20 amino acids used plus a stop codon (null amino acid) out of 
the 64. The rest of the 43 codes are used as synonyms of the 21 useful 
ones. Whereas 6 bits are required to code for 64 possible 

combinations, only about 4.4 (log 2 21) bits are required to code for 21 
possibilities, a savings of 1.6 out of 6 bits (about 27 percent), bringing 
us down to about 15 million bytes. In addition, some standard 
compression based on repeating sequences is feasible here, although 
much less compression is possible on this protein-coding portion of the 
DNA than in the so-called junk DNA, which has massive 
redundancies. So this will bring the figure probably below 12 million 
bytes. However, now we have to add information for the noncoding 
portion of the DNA that controls gene expression. Although this 
portion of the DNA constitutes the bulk of the genome, it appears to 
have a low level of information content and is replete with massive 
redundancies. Estimating that it matches the approximately 12 million 
bytes of protein-coding DNA, we again come to approximately 24 
million bytes. From this perspective, an estimate of 30 to 100 million 
bytes is conservatively high. 

8, Dharmendra S. Modha et al., “Cognitive Computing,” 
Communications of the ACM 54, no. 8 (2011): 62-71, 

9* Kurzweil, The Singularity Is Near, chapter 9, section titled “The 
Criticism from Ontology: Can a Computer Be Conscious?” (pp. 

10, Michael Denton, “Organism and Machine: The Flawed Analogy,” 

in Are We Spiritual Machines? Ray Kurzweil vs. the Critics of 
Strong AT (Seattle: Discovery Institute, 2002). 

11. Hans Moravec, Mind Children (Cambridge, MA: Harvard 
University Press, 1988). 


L “In U.S., Optimism about Future for Youth Reaches All-Time Low,” 
Gallup Politics, May 2, 2011, 

T James C. Riley, Rising Life Expectancy: A Global History 
(Cambridge: Cambridge University Press, 2001). 

T J. Bradford DeLong, “Estimating World GDP, One Million B.C.— 
Present,” May 24, 1998, 
growth.xhtml. See also Peter H. Diamandis and Steven Kotler, 
Abundance: The Future Is Better Than You Think (New York: Free 
Press, 2012). 

4 Martine Rothblatt, Transgender to Transhuman (privately printed, 
2011). She explains how a similarly rapid trajectory of acceptance is 
most likely to occur for “transhumans,” for example, nonbiological 
but convincingly conscious minds as discussed in chapter 9. 

5, The following excerpt from The Singularity Is Near, chapter 3 (pp. 
133-35), by Ray Kurzweil (New York: Viking, 2005), discusses the 
limits of computation based on the laws of physics: 

The ultimate limits of computers are profoundly high. Building 
on work by University of California at Berkeley Professor Hans 
Bremermann and nanotechnology theorist Robert Freitas, MIT 
Professor Seth Lloyd has estimated the maximum computational 
capacity, according to the known laws of physics, of a computer 
weighing one kilogram and occupying one liter of volume—about the 
size and weight of a small laptop computer—what he calls the 
“ultimate laptop.” 

[Note: Seth Lloyd, “Ultimate Physical Limits to Computation,” 
Nature 406 (2000): 1047-54. 

[Early work on the limits of computation were done by Hans J. 
Bremermann in 1962: Hans J. Bremermann, “Optimization Through 
Evolution and Recombination,” in M. C. Yovits, C. T. Jacobi, C. D. 

Goldstein, eds., Self-Organizing Systems (Washington, D.C.: Spartan 
Books, 1962), pp. 93-106. 

[In 1984 Robert A. Freitas Jr. built on Bremermann’s work in 
Robert A. Freitas Jr., “Xenopsychology,” Analog 104 (April 1984): 


The potential amount of computation rises with the available 
energy. We can understand the link between energy and computational 
capacity as follows. The energy in a quantity of matter is the energy 
associated with each atom (and subatomic particle). So the more 
atoms, the more energy. As discussed above, each atom can potentially 
be used for computation. So the more atoms, the more computation. 
The energy of each atom or particle grows with the frequency of its 
movement: the more movement, the more energy. The same 
relationship exists for potential computation: the higher the frequency 
of movement, the more computation each component (which can be an 
atom) can perform. (We see this in contemporary chips: the higher the 
frequency of the chip, the greater its computational speed.) 

So there is a direct proportional relationship between the energy 
of an object and its potential to perform computation. The potential 
energy in a kilogram of matter is very large, as we know from 
Einstein’s equation E = me 2 . The speed of light squared is a very large 
number: approximately 10 17 meter 2 /second 2 . The potential of matter to 
compute is also governed by a very small number, Planck’s constant: 
6.6 x 10“ 34 joule-seconds (a joule is a measure of energy). This is the 
smallest scale at which we can apply energy for computation. We 
obtain the theoretical limit of an object to perform computation by 
dividing the total energy (the average energy of each atom or particle 
times the number of such particles) by Planck’s constant. 

Lloyd shows how the potential computing capacity of a kilogram 
of matter equals pi times energy divided by Planck’s constant. Since 
the energy is such a large number and Planck’s constant is so small, 
this equation generates an extremely large number: about 5 x lO 50 
operations per second. 

[Note: tt x maximum energy (10 17 kg x meter 2 /second 2 ) / (6.6 x 
1(T 34 ) joule-seconds) = ~ 5 x 10 50 operations/second.] 

If we relate that figure to the most conservative estimate of 
human brain capacity (10 19 cps and lO 10 humans), it represents the 

equivalent of about 5 billion trillion human civilizations. 

[Note: 5 x 10 50 cps is equivalent to 5 x 10 21 (5 billion trillion) 
human civilizations (each requiring 10 29 cps).] 

If we use the figure of 10 16 cps that I believe will be sufficient for 
functional emulation of human intelligence, the ultimate laptop would 
function at the equivalent brain power of 5 trillion trillion human 

[Note: Ten billion (10 10 ) humans at 10 16 cps each is 10 26 cps for 
human civilization. So 5 x 10 5 ° cps is equivalent to 5 x 10 24 (5 trillion 
trillion) human civilizations.] 

Such a laptop could perform the equivalent of all human thought 
over the last ten thousand years (that is, ten billion human brains 
operating for ten thousand years) in one ten-thousandth of a 

[Note: This estimate makes the conservative assumption that 
we’ve had ten billion humans for the past ten thousand years, which is 
obviously not the case. The actual number of humans has been 
increasing gradually over the past to reach about 6.1 billion in 2000. 
There are 3 x 10 7 seconds in a year, and 3 x 10 11 seconds in ten 
thousand years. So, using the estimate of 10 26 cps for human 
civilization, human thought over ten thousand years is equivalent to 
certainly no more than 3 x 10 37 calculations. The ultimate laptop 
performs 5 x 10 5 ° calculations in one second. So simulating ten 
thousand years of ten billion humans’ thoughts would take it about 10” 
13 seconds, which is one ten-thousandth of a nanosecond.] 

Again, a few caveats are in order. Converting all of the mass of 
our 2.2-pound laptop into energy is essentially what happens in a 
thermonuclear explosion. Of course, we don’t want the laptop to 
explode but to stay within its one-liter dimension. So this will require 
some careful packaging, to say the least. By analyzing the maximum 
entropy (degrees of freedom represented by the state of all the 
particles) in such a device, Lloyd shows that such a computer would 
have a theoretical memory capacity of 10 31 bits. It’s difficult to 
imagine technologies that would go all the way in achieving these 
limits. But we can readily envision technologies that come reasonably 
close to doing so. As the University of Oklahoma project shows, we 
already demonstrated the ability to store at least fifty bits of 
information per atom (although only on a small number of atoms, so 

far). Storing 10 27 bits of memory in the 10 25 atoms in a kilogram of 
matter should therefore be eventually achievable. 

But because many properties of each atom could be exploited to 
store information—such as the precise position, spin, and quantum 
state of all of its particles—we can probably do somewhat better than 
10 27 bits. Neuroscientist Anders Sandberg estimates the potential 
storage capacity of a hydrogen atom at about four million bits. These 
densities have not yet been demonstrated, however, so weTl use the 
more conservative estimate. 

[Note: Anders Sandberg, “The Physics of the Information 
Processing Superobjects: Daily Life Among the Jupiter Brains,” 
Journal of Evolution and Technology 5 (December 22, 1999),] 

As discussed above, 10 42 calculations per second could be 
achieved without producing significant heat. By fully deploying 
reversible computing techniques, using designs that generate low 
levels of errors, and allowing for reasonable amounts of energy 
dissipation, we should end up somewhere between 10 42 and 10 50 
calculations per second. 

The design terrain between these two limits is complex. 
Examining the technical issues that arise as we advance from 10 42 to 
10 50 is beyond the scope of this chapter. We should keep in mind, 
however, that the way this will play out is not by starting with the 
ultimate limit of 10 50 and working backward based on various 
practical considerations. Rather, technology will continue to ramp up, 
always using its latest prowess to progress to the next level. So once 
we get to a civilization with 10 42 cps (for every 2.2 pounds), the 
scientists and engineers of that day will use their essentially vast 
nonbiological intelligence to figure out how to get 10 43 , then 10 44 , and 
so on. My expectation is that we will get very close to the ultimate 

Even at 10 42 cps, a 2.2-pound “ultimate portable computer” 
would be able to perform the equivalent of all human thought over the 
last ten thousand years (assumed at ten billion human brains for ten 
thousand years) in ten microseconds. 

[Note: See note above. 10 42 cps is a factor of 1CT 8 less than 10 5 ° 
cps, so one ten-thousandth of a nanosecond becomes 10 

If we examine the Exponential Growth of Computing chart 
( chapter 2 ). we see that this amount of computing is estimated to be 
available for one thousand dollars by 2080. 


Page numbers in italics refer to graphs and illustrations, 
abortion, 212-13 

Abundance (Diamandis and Kotler), 278 
Ackerman, Diane, 179 
ACTH (adrenocorticotropin), 107 
addictive behaviors, 105-6, 118 
adrenal glands, 107 
adrenaline, 107 

Age of Intelligent Machines, The (Kurzweil), 4, 165-66, 256-57 

Age of Spiritual Machines, The (Kurzweil), 4, 257, 267 

A.I. (film), 210 

Aiken, Howard, 189 

Alexander, Richard D., 224 

algorithms, intelligent, 6-7 

Allen, Paul, 266-72 

Allman, John M., 179 

Alzheimer’s disease, 102 

amygdala, 71, 77, 106-8, 109 

analog processing, digital emulation of, 194-95, 274 

Analytical Engine, 189-90 

anesthesia awareness, 206 

animal behavior, evolution of, 122 

apical dendrites, 110 

aptitude, 111-12 

artificial intelligence (AI), 7, 37-38, 50, 265, 280 

Allen on, 270-71 

biological models for, 273 

chess playing and, 6, 38-39, 165-66, 257 

conversation and, 168-69 

as extension of neocortex, 172, 276 
knowledge bases and, 4, 6-7, 170-71, 246, 247 

language and speech processing in, 72-73, 92, 115-16, 122-23, 128, 135- 
41, 142-46, 145, 149-50, 152-53, 156, 157-72 
medicine and, 6-7, 39, 108, 156, 160-61, 168 
omnipresence of, 158 
optimization of pattern recognition in, 112 
sparse coding in, 95-96 
see also neocortex, digital 
Audience, Inc., 96-97, 98 
auditory association, 77 
auditory cortex, 7, 77, 97, 128 
auditory information processing, 96-97, 97 
auditory nerve, 97, 128 
data reduction in, 138 
auditory pathway, 97 
autoassociation, 59-61, 133, 173 
automobiles, self-driving, 7, 159, 261, 274 
axons, 36, 42, 43, 66, 67, 90, 100, 113, 150, 173 
as digital processors, 191 

Babbage, Charles, 189-90 
Bainbridge, David, 179 
bandwidth, of Internet, 254 
basis functions, 103-4 
Bedny, Marina, 87 
Bell System Technical Journal, 184 
Berger, Theodore, 102 
Berners-Lee, Tim, 172 
Bernoulli’s principle, 5, 8 

Better Angels of Our Nature: Why Violence Has Declined (Pinker), 27 

Bierce, Ambrose, 66 

BINAC, 189 

Bing, 171 

biology, 37 

DNA as unifying theory of, 17 
reverse-engineering of, 4-5 
biomedicine, LOAR and, 251, 252, 253 
Blackmore, Susan, 211 

Blade Runner (film), 210 
Blakeslee, Sandra, 73, 156 
Blue Brain Project, 63, 80, 124-28 ,125 
Bombe, 187 

Bostrom, Nick, 129-30, 222 

Boyden, Ed, 126 

brain, evolution of, 2 

brain, human: 

analog computing in, 274 

complexity of, 8-9, 181, 272 

digital implants in, 243-44 

digital neocortex as extension of, 172, 276 

hemispheres of, 77, 224-49 

LOAR as applied to, 261-63, 263, 264, 265 

prediction by, 250 

redundancy of, 9 

reverse-engineering of, see brain, human, computer emulation of; 

neocortex, digital 
structure of, 77 

brain, human, computer emulation of, 5, 7, 179-98, 273, 280 

invariance and, 197 

memory requirements of, 196-97 

parallel processing in, 197 

processing speed in, 195-96 

redundancy in, 197 

singularity and, 194 

Turing test and, 159-60, 169, 170, 178, 191, 213, 214, 233, 276, 298n 
von Neumann on, 191-95 
see also neocortex, digital 
brain, mammalian: 

hierarchical thinking as unique to, 2-3, 35 
neocortex in, 78, 93, 286n 

brain plasticity, 79, 87-89, 91, 182, 193, 197, 225, 280 

as evidence of universal neocortical processing, 86, 88, 152 

limitations on, 88-89 

brain scanning, 7, 263, 308 n 

destructive, 264, 265, 309n-lln 

LOAR and, 262-63, 263, 264, 265 

nondestructive, 127, 129, 264, 312n-13n 

noninvasive, 273 

Venn diagram of, 262 

brain simulations, 124-31, 262 

brain stem, 36, 99 

Bremermann, Hans, 316n 

Britain, Battle of, 187 

Brodsky, Joseph, 199 

Burns, Eric A., 113 

busy beaver problem, 207 

Butler, Samuel, 62, 199-200, 224, 248-49 

Byron, Ada, Countess of Lovelace, 190, 191 

California, University of, at Berkeley, 88 
“CALO” project, 162 

carbon atoms, information structures based on, 2 

Carroll, Lewis, 109 

cells, replacement of, 245, 246 

cellular automata, 236-39 

cerebellum, 7, 77, 103-4 

uniform structure of, 103 

cerebral cortex, 7-8 

see also neocortex 

Chalmers, David, 201-2, 218, 241 

“chatbots,” 161 

chemistry, 37 

chess, AI systems and, 6, 38-39, 165-66, 257 


language and, 3, 41 

tool use by, 41 

“Chinese room” thought experiment, 170, 274-75 

Chomsky, Noam, 56, 158 

Church, Alonzo, 186 

Church-Turing thesis, 186 

civil rights, 278 

cloud computing, 116-17, 123, 246, 279-80 

cochlea, 96, 97, 135, 138 

cochlear implants, 243 

Cockburn, David, 214 

Cold Spring Harbor Laboratory, 129 

Colossus, 187, 188 
“common sense,” 40 

communication, reliability of, 182-85, 190 

communication technology, LOAR and, 253, 254 

compatibilism, 234 

complexity, 198, 233 

of human brain, 8-9, 181, 272 

modeling and, 37-38 

true vs. apparent, 10-11 


price/performance of, 4-5, 250-51, 257, 257, 267-68, 301n-3n 
thinking compared with to, 26-27 
universality of, 26, 181-82, 185, 188, 192, 207 
Computer and the Brain, The (von Neumann), 191 

brain emulated by, see brain, human, computer emulation of 
consciousness and, 209-11, 213-15, 223 
intelligent algorithms employed by, 6-7 
knowledge base expanded by, 4, 246, 247 
logic gates in, 185 

memory in, 185, 259, 260, 268, 301n-3n, 306n-7n 
reliability of communication by, 182-85, 190 
see also neocortex, digital 

“Computing Machinery and Intelligence” (Turing), 191 

conditionals, 65, 69, 153, 189, 190 

confabulation, 70, 217, 227, 228, 229 

connectionism, 133, 191 

“connectome,” 262 

consciousness, 11, 199-209 

cerebral hemispheres and, 226-29 

computers and, 209-11, 213-15, 223, 233 

Descartes on, 221-22 

dualist views of, 202-3 

Eastern vs. Western views of, 218-24 

free will and, 233-34 

Kurzweil’s thought experiment on, 210 

leap-of-faith view of, 209-10, 233 

as meme, 211, 235 

memory and, 28-29, 206-7, 217 

moral and legal systems as based on, 212-13 

of nonhuman life-forms, 213-14 

panprotopsychist view of, 203, 213 

as philosophical construct, 201-9 

qualia and, 203-5, 211 

as scientifically unverifiable, 205, 211, 228 

as spiritual construct, 222-23 

as subjective experience, 211 

Wittgenstein on, 220-21 

zombie thought experiment and, 202 

conversation, AI and, 168-69 

convictions, courage of, 11, 23-24, 112, 117 

Cooper’s law, 253 

corpus callosum, 70, 77, 226 

cortical association areas, 58 

cortisol, 107 

Craig, Arthur, 100 

creativity, 113-17 

and expansion of neocortex, 116-17 
Cretaceous-Paleogene extinction event, 79 
Crick, Francis, 16-17 
critical thinking, 6, 176, 197 
“criticism from incredulity,” 266-72 
Crookes radiometer, 20-21, 21 
crossbar switching, 85 
Curtiss, Susan, 225 
Cybernetics (Wiener), 115 
Cyc project, 162, 164 

D2 gene, 106 

Dalai Lama, 109 

Dalrymple, David, 124, 291n 

DARPA, 162, 163 

Darwin, Charles, 15 

Lyell’s influence on, 14-15, 114, 177 

thought experiments of, 14-16, 23 

data, determined vs. predictable, 26, 239 

data traffic, on Internet, 254 

de Bode, Stella, 225 

Deep Blue, 39, 166 
DeMille, Cecil B., 113 
dendrites, 42, 43, 66, 67, 90, 150 
as analog processors, 191-92 
apical, 110 

Dennett, Daniel, 205-6, 230, 234 

Denton, Michael, 275-76 

Descartes, Rene, 221-22, 240 

destructive imaging techniques, 264, 265, 309n-lln 

determined outcomes, predictable outcomes vs., 26, 239 

determinism, 232-33 

free will and, 232-33, 234 

randomness and, 236 

Devil’s Dictionary, The (Bierce), 66 

Diamandis, Peter, 278 

Diamond, Marian, 25 

Dickinson, Emily, 1 

diffusion tractography, 129 

digital processors: 

emulation of analog processing in, 194-95, 274 
see also computers; neocortex, digital 
Diogenes Laertius, 246 
DNA, 9-10 

animal behavior encoded in, 122 
discovery and description of, 16-17 
encoding of information in, 2, 17 
as unifying theory of biology, 17 
see also genome, human 
dopamine, 105-6, 107, 118 
Dostoevsky, Fyodor, 199 
Dragon Dictation, 152-53 
Dragon Go!, 162, 164 
Dragon Naturally Speaking, 152 
Drave, Scott, 149 

conscious thinking vs., 71-72 

taboos and, 71-72 

as undirected thoughts, 70-72 

dynamic RAM memory, growth in, 259, 301n-3n 

E = me 2, 22 
Eckert, J. Presper, 189 
EDSAC, 189 
EDVAC, 189 

Einstein, Albert, 11, 25, 35, 71 

thought experiments of, 18-23, 114, 117 

Electric Sheep, 149 

Emerson, Ralph Waldo, 13 

emotional intelligence, 110, 194, 201, 213 


high-level, 109-11 

as products of both old brain and neocortex, 107-8 

energy, mass equivalent of, 22-23 

ENIAC, 180, 189 

Enigma coding machine, 187 

EPAM (elementary perceiver and memorizer), 37-38 

estrogen, 118 

ether theory, Michelson-Morley disproof of, 18, 19, 36, 114 

evolution, 76-79 

Darwin’s theory of, 14-16 

encoding of information and, 2 

intelligence as goal of, 76-78, 277, 278 

LOAR and, 4 

of neocortex, 35-36 

of simulated organisms, 147-53 

as spiritual process, 223 

survival as goal of, 79, 104, 242 

see also natural selection 

evolutionary (genetic) algorithms (GAs), 147-53, 173 
existentialism, 221 

expectation (excitatory) signals, 42, 52, 54, 60, 67, 73, 85, 91, 100, 112, 
173, 175, 196-97 
expert managers, 166-67, 168 
experts, core knowledge of, 39-40 
exponential growth, see law of accelerating returns 
eye movement, pattern recognition and, 73 

Far Side, 277 

fear, in old and new brains, 104-8 

feature invariance, see invariance 
feedforward neural net, 134, 135 
Feldman, Daniel E., 88 
Felleman, Daniel J., 86 
fetus, brain of, 62 

field programmable gate array (FPGA), 83 

“fight or flight” mechanism, 107, 118 

“First Draft of a Report on EDVAC” (von Neumann), 188 

Forest, Craig, 126 

formants, 135 ,137 

fractals, 9, 10-11 ,10 

Franklin, Rosalind, 16-17 ,17 

free will, 11, 224-40 

consciousness and, 233-34 

definition of, 231-32 

determinism and, 232-33, 234 

as meme, 235 

responsibility and, 235 

Freitas, Robert, 316n 

Freud, Sigmund, 66, 71, 72 

Friston, K. J., 75 

frontal lobe, 36, 41, 77 

fusiform gyrus, 89, 95, 111 

gambling, 106 
ganglion cells, 95 

Garcia Marquez, Gabriel, 3-4, 283n-85n 
Gazzaniga, Michael, 226-29, 234 
General Electric, 149 

genetic (evolutionary) algorithms, see evolutionary (genetic) algorithms 
genome, human, 4, 103, 251 

design information encoded in, 90, 147, 155, 271, 314n-15n 

redundancy in, 271, 314n, 315n 

sequencing of, LOAR and, 252, 252, 253 

see also DNA 

George, Dileep, 41, 73, 156 

Ginet, Carl, 234 

God, concept of, 223 

Godel, Kurt, 187 

incompleteness theorem of, 187, 207-8 
“God parameters,” 147 
Good, Irvin J., 280-81 
Google, 279 

self-driving cars of, 7, 159, 261, 274 
Google Translate, 163 
Google Voice Search, 72, 161 
Greaves, Mark, 266-72 
Grossman, Terry, 287n-88n 
Grotschel, Martin, 269 

Hameroff, Stuart, 206, 208, 274 
Hameroff-Penrose thesis, 208-9 
Hamlet (Shakespeare), 209 
Harnad, Stevan, 266 

Harry Potter and the Half-Blood Prince (Rowling), 117 

Harry Potter and the Prisoner ofAzkaban (Rowling), 121 

Hasson, Uri, 86 

Havemann, Joel, 25 

Hawkins, Jeff, 41, 73, 156 

Hebb, Donald 0., 79-80 

Hebbian learning, 80 

hemispherectomy, 225 

hidden Markov models (HMMs), 68, 141-44, 143,145, 147, 162 
hierarchical hidden Markov models (HHMMs), 51, 68, 72, 74, 144-46, 
149-50, 152-53, 155, 156, 162, 164, 167-68, 195, 269, 270 
pmning of unused connections by, 144, 147, 155 
hierarchical learning, 164, 195, 197 
hierarchical memory: 
digital, 156-57 
temporal, 73 

hierarchical systems, 4, 35 

hierarchical thinking, 8, 69, 105, 117, 153-54, 177, 233, 286n 

bidirectional flow of information in, 52 

language and, 56, 159, 162, 163 

in mammalian brain, 2-3 

pattern recognition as, 33, 41-53 

recursion in, 3, 7-8, 56, 65, 91, 109, 156, 177 

routine tasks and, 32-33 

as survival mechanism, 79 

as unique to mammalian brain, 35 

hippocampus, 63, 77, 101-2 

Hitchhiker’s Guide to the Galaxy, The (Adams), 161 

Hobbes, Thomas, 278 

Hock, Dee, 113 

Horwitz, B., 75 

Hubei, David H., 34 

Human Connectome Project, 129 

human genome, see genome, human 

Human Genome Project, 251, 273 


merger of intelligent technology with, 266-72, 276, 279-82 
tool-making ability of, 3, 27, 276, 279 
Hume, David, 234-35 

IBM, 6-7, 108, 128, 165-66 

Cognitive Computing Group of, 195 

ideas, recursive linking of, 3 

identity, 10, 11, 240-47 

as pattern continuity, 246, 247 

thought experiments on, 242-47 

importance parameters, 42, 60, 66, 67 

incompatibilism, 234, 236 

incompleteness, GodePs theorem of, 187, 207-8 

inference engines, 162-63 

information, encoding of: 

in DNA, 2, 17, 122 

evolution and, 2 

in human genome, 90, 147, 155, 271, 314n-15n 
information structures, carbon-based, 2 
information technologies: 
exponential growth of, 278-79 

LOAR and, 4, 249-57, 252, 257, 258, 259, 260, 261, 261 

inhibitory signals, 42, 52-53, 67, 85, 91, 100, 173 

insula, 99-100, 99, 110 

integrated circuits, 85 

Intel, 268 

intelligence, 1-2 

emotional, 110, 194, 201, 213 
as evolutionary goal, 76-78, 277, 278 
evolution of, 177 
as problem-solving ability, 277 

International Dictionary of Psychology (Sutherland), 211 
“International Technology Roadmap for Semiconductors,” 268 
Internet, exponential growth of, 254 
Interpretation of Dreams, The (Freud), 66 
intuition, linear nature of, 266 

invariance, in pattern recognition, 30, 59-61, 133, 135, 137, 175 

and computer emulation of brain, 197 

one-dimensional representations of data and, 141-42 

vector quantization and, 141 

inventors, timing and, 253, 255 

I, Robot (film), 210 

Jacquard loom, 189, 190 
James, William, 75-76, 98-99 
Jeffers, Susan, 104 
Jennings, Ken, 157-58, 165 

Jeopardy! (TV show), 6-7, 108, 157-58, 160, 165, 166, 167, 168, 169, 172, 
178, 232-33, 270 
Joyce, James, 55 

Kasparov, Garry, 39, 166 
K Computer, 196 
knowledge bases: 

AI systems and, 4, 6-7, 170-71, 246, 247 

of digital neocortex, 177 

exponential growth of, 3 

as inherently hierarchical, 220 

language and, 3 

professional, 39-40 

as recursively linked ideas, 3 

Kodandaramaiah, Suhasa, 126 

Koene, Randal, 89 

Koltsov, Nikolai, 16 

Kotler, Steven, 278, 161 

Kurzweil Applied Intelligence, 144 
Kurzweil Computer Products, 122 
Kurzweil Voice, 160 

lamina 1 neurons, 97 

chimpanzees and, 3, 41 

and growth of knowledge base, 3 

hierarchical nature of, 56, 159, 162, 163 

as metaphor, 115 

as translation of thinking, 56, 68 

language software, 51, 72-73, 92, 115-16, 122-23, 144-45, 145, 156, 157- 
72, 174, 270 

expert managers in, 166-67 
hand-coded rules in, 164-65, 166, 168 
HHMMs in, 167-68 
hierarchical systems in, 162-65 
Larson, Gary, 277 

“Last Voyage of the Ghost, The” (Garcia Marquez), 3-4 

lateral geniculate nucleus, 95, 100 

law of accelerating returns (LOAR), 4, 6, 7, 41, 123 

as applied to human brain, 261-63, 263, 264, 265 

biomedicine and, 251, 252, 253 

communication technology and, 253, 254 

computation capacity and, 281, 316n-19n 

information technology and, 4, 249-57, 252, 257, 258, 259, 260, 261, 261 
objections to, 266-82 

predictions based on, 256-57, 257, 258, 259, 260, 261 
and unlikelihood of other intelligent species, 5 
“Law of Accelerating Returns, The” (Kurzweil), 267 
laws of thermodynamics, 37, 267 
learning, 61-65, 122, 155, 273-74 
conditionals in, 65 

and difficulty of grasping more than one conceptual level at a time, 65 
in digital neocortex, 127-28, 175-76 
environment and, 119 
Hebbian, 80 

hierarchical, 164, 195, 197 
in neural nets, 132-33 

neurological basis of, 79-80 

pattern recognition as basic unit of, 80-81 

of patterns, 63-64, 90 

recognition as simultaneous with, 63 

simultaneous processing in, 63, 146 

legal systems, consciousness as basis of, 212-13 

Leibniz, Gottfried Wilhelm, 34, 223 

Lenat, Douglas, 162 

Leviathan (Hobbes), 278 

Lewis, Al, 93 

Libet, Benjamin, 229-30, 231, 234 
light, speed of, 281 

Einstein’s thought experiments on, 18-23 

linear programming, 64 

LISP (LISt Processor), 153-55, 163 

pattern recognition modules compared with, 154, 155 

Lloyd, Seth, 316n, 317n 

Loebner, Hugh, 298n 

Loebner Prize, 298n 

logic, 38-39 

logical positivism, 220 

logic gates, 185 

Lois, George, 113 

love, 117-20 

biochemical changes associated with, 118-19 

evolutionary goals and, 119 

pattern recognition modules and, 119-20 

“Love Is the Drug,” 118 

Lovelace, Ada Byron, Countess of, 190, 191 

lucid dreaming, 72, 287n-88n 

Lyell, Charles, 14-15, 114, 177 

McCarthy, John, 153 
McClelland, Shearwood, 225 
McGinn, Colin, 200 

magnetic data storage, growth in, 261, 301n-3n 
magnetoencephalography, 129 
Manchester Small-Scale Experimental Machine, 189 
Mandelbrot set, 10-11 ,10 

Marconi, Guglielmo, 253 
Mark 1 Perceptron, 131-32, 134, 135, 189 
Markov, Andrei Andreyevich, 143 
Markram, Henry, 80-82, 124-27, 129 
mass equivalent, of energy, 22-23 
Mathematica, 171 

“Mathematical Theory of Communication, A” (Shannon), 184 

Mauchly, John, 189 

Maudsley, Henry, 224 

Maxwell, James Clerk, 20 

Maxwell, Robert, 225 

Mead, Carver, 194-95 

medial geniculate nucleus, 97, 100 

medicine, AI and, 6-7, 39, 108, 156, 160-61, 168 


consciousness as, 211, 235 
free will as, 235 

memory, in computers, 185, 259, 260, 268, 301n-3n, 306n-7n 

memory, memories, human: 

abstract concepts in, 58-59 

capacity of, 192-93 

computers as extensions of, 169 

consciousness vs., 28-29, 206-7, 217 

dimming of, 29, 59 

hippocampus and, 101-2 

as ordered sequences of patterns, 27-29, 54 

redundancy of, 59 

unexpected recall of, 31-32, 54, 68-69 
working, 101 
Menabrea, Luigi, 190 
metacognition, 200, 201 
metaphors, 14-15, 113-17, 176-77 
Michelson, Albert, 18, 19, 36, 114 
Michelson-Morley experiment, 19, 36, 114 
microtubules, 206, 207, 208, 274 
Miescher, Friedrich, 16 
mind, 11 

pattern recognition theory of (PRTM), 5-6, 8, 11, 34-74, 79, 80, 86, 92, 
111, 172, 217 

thought experiments on, 199-247 

mind-body problem, 221 

Minsky, Marvin, 62, 133-35, 134, 199, 228 

MIT Artificial Intelligence Laboratory, 134 

MIT Picower Institute for Learning and Memory, 101 

MobilEye, 159 

modeling, complexity and, 37-38 

Modha, Dharmendra, 128, 195, 271-72 

momentum, 20-21 

conservation of, 21-22 

Money, John William, 118, 119 

montane vole, 119 

mood, regulation of, 106 

Moore, Gordon, 251 

Moore’s law, 251, 255, 268 

moral intelligence, 201 

moral systems, consciousness as basis of, 212-13 

Moravec, Hans, 196 

Morley, Edward, 18, 19, 36, 114 

Moskovitz, Dustin, 156 

motor cortex, 36, 99 

motor nerves, 99 

Mountcastle, Vernon, 36, 37, 94 

Mozart, Leopold, 111 

Mozart, Wolfgang Amadeus, 111, 112 

MRI (magnetic resonance imaging), 129 

spatial resolution of, 262-65, 263, 309n 

MT (V5) visual cortex region, 83, 95 

Muckli, Lars, 225 

music, as universal to human culture, 62 
mutations, simulated, 148 

names, recalling, 32 
National Institutes of Health, 129 
natural selection, 76 

geologic process as metaphor for, 14-15, 114, 177 
see also evolution 
Nature, 94 

nematode nervous system, simulation of, 124 

neocortex, 3, 7, 77, 78 

AI reverse-engineering of, see neocortex, digital 
bidirectional flow of information in, 85-86, 91 
evolution of, 35-36 

expansion of, through AI, 172, 266-72, 276 

expansion of, through collaboration, 116 

hierarchical order of, 41-53 

learning process of, see learning 

linear organization of, 250 

as metaphor machine, 113 

neural leakage in, 150-51 

old brain as modulated by, 93-94, 105, 108 

one-dimensional representations of multidimensional data in, 53, 66, 91, 

pattern recognition in, see pattern recognition 

pattern recognizers in, see pattern recognition modules 

plasticity of, see brain plasticity 

prediction by, 50-51, 52, 58, 60, 66-67, 250 

PRTM as basic algorithm of, 6 

pruning of unused connections in, 83, 90, 143, 174 

redundancy in, 9, 224 

regular grid structure of, 82-83, 84, 85, 129, 262 
sensory input in, 58, 60 

simultaneous processing of information in, 193 

specific types of patterns associated with regions of, 86-87, 89-90, 91, 111, 

structural simplicity of, 11 

structural uniformity of, 36-37 

structure of, 35-37, 38, 75-92 

as survival mechanism, 79, 250 

thalamus as gateway to, 100-101 

total capacity of, 40, 280 

total number of neurons in, 230 

unconscious activity in, 228, 231, 233 

unified model of, 24, 34-74 

as unique to mammalian brain, 93, 286n 

universal processing algorithm of, 86, 88, 90-91, 152, 272 

see also cerebral cortex 

neocortex, digital, 6-8, 41, 116-17, 121-78, 195 

benefits of, 123-24, 247 

bidirectional flow of information in, 173 

as capable of being copied, 247 

critical thinking module for, 176, 197 

as extension of human brain, 172, 276 

HHMMs in, 174-75 

hierarchical structure of, 173 

knowledge bases of, 177 

learning in, 127-28, 175-76 

metaphor search module in, 176-77 

moral education of, 177-78 

pattern redundancy in, 175 

simultaneous searching in, 177 

structure of, 172-78 

virtual neural connections in, 173-74 

neocortical columns, 36-37, 38, 90, 124-25 

nervous systems, 2 

neural circuits, unreliability of, 185 

neural implants, 243, 245 

neural nets, 131-35, 144, 155 

algorithm for, 291n-97n 

feedforward, 134, 135 

learning in, 132-33 

neural processing: 

digital emulation of, 195-97 

massive parallelism of, 192, 193, 195 

speed of, 192, 195 

neuromorphic chips, 194-95, 196 

neuromuscular junction, 99 

neurons, 2, 36, 38, 43, 80, 172 

neurotransmitters, 105-7 

new brain, see neocortex 

Newell, Allen, 181 

New Kind of Science, A (Wolfram), 236, 239 
Newton, Isaac, 94 
Nietzsche, Friedrich, 117 

nonbiological systems, as capable of being copied, 247 
nondestructive imaging techniques, 127, 129, 264, 312n-13n 
nonmammals, reasoning by, 286n 

noradrenaline, 107 
norepinephrine, 118 

Notes from Underground (Dostoevsky), 199 

Nuance Speech Technologies, 6-7, 108, 122, 152, 161, 162, 168 

nucleus accumbens, 77, 105 

Numenta, 156 

NuPIC, 156 

obsessive-compulsive disorder, 118 

occipital lobe, 36 

old brain, 63, 71, 90, 93-108 

neocortex as modulator of, 93-94, 105, 108 

sensory pathway in, 94-98 

olfactory system, 100 

Oluseun, Oluseyi, 204 

OmniPage, 122 

One Hundred Years of Solitude (Garcia Marquez), 283n-85n 

On Intelligence (Hawkins and Blakeslee), 73, 156 

On the Origin of Species (Darwin), 15-16 

optical character recognition (OCR), 122 

optic nerve, 95, 100 

channels of, 94-95, 96 

organisms, simulated, evolution of, 147-53 

overfitting problem, 150 

oxytocin, 119 

pancreas, 37 

panprotopsychism, 203, 213 
Papert, Seymour, 134-35 ,134 
parameters, in pattern recognition: 

“God,” 147 

importance, 42, 48-49, 60, 66, 67 

size, 42, 49-50, 60, 61, 66, 67, 73-74, 91-92, 173 

size variability, 42, 49-50, 67, 73-74, 91-92 

Parker, Sean, 156 

Parkinson’s disease, 243, 245 

particle physics, see quantum mechanics 

Pascal, Blaise, 117 

patch-clamp robotics, 125-26 ,126 

pattern recognition, 195 

of abstract concepts, 58-59 

as based on experience, 50, 90, 273-74 

as basic unit of learning, 80-81 

bidirectional flow of information in, 52, 58, 68 

distortions and, 30 

eye movement and, 73 

as hierarchical, 33, 90, 138, 142 

of images, 48 

invariance and, see invariance, in pattern recognition 

learning as simultaneous with, 63 

list combining in, 60-61 

in neocortex, see pattern recognition modules 

redundancy in, 39-40, 57, 60, 64, 185 

pattern recognition modules, 35-41, 42, 90, 198 

autoassociation in, 60-61 

axons of, 42, 43, 66, 67, 113, 173 

bidirectional flow of information to and from thalamus, 100-101 
dendrites of, 42, 43, 66, 67 
digital, 172-73, 175, 195 

expectation (excitatory) signals in, 42, 52, 54, 60, 67, 73, 85, 91, 100, 112, 
173, 175, 196-97 

genetically determined structure of, 80 

“God parameter” in, 147 

importance parameters in, 42, 48-49, 60, 66, 67 

inhibitory signals in, 42, 52-53, 67, 85, 91, 100, 173 

input in, 41-42, 42, 53-59 

love and, 119-20 

neural connections between, 90 

as neuronal assemblies, 80-81 

one-dimensional representation of multidimensional data in, 53, 66, 91, 

prediction by, 50-51, 52, 58, 60, 66-67 
redundancy of, 42, 43, 48, 91 
sequential processing of information by, 266 
simultaneous firings of, 57-58, 57, 146 

size parameters in, 42, 49-50, 60, 61, 66, 67, 73-74, 91-92, 173 
size variability parameters in, 42, 67, 73-74, 91-92, 173 
of sounds, 48 

thresholds of, 48, 52-53, 60, 66, 67, 111-12, 173 
total number of, 38, 40, 41, 113, 123, 280 
universal algorithm of, 111, 275 

pattern recognition theory of mind (PRTM), 5-6, 8, 11, 34-74, 79, 80, 86, 
92, 111, 172, 217 

hierarchical ordering of, 41-53 

higher-level patterns attached to, 43, 45, 66, 67 

input in, 41, 42, 44, 66, 67 

learning of, 63-64, 90 

name of, 42-43 

output of, 42, 44, 66, 67 

redundancy and, 64 

specific areas of neocortex associated with, 86-87, 89-90, 91, 111, 152 

storing of, 64-65 

structure of, 41-53 

Patterns, Inc., 156 

Pavlov, Ivan Petrovich, 216 

Penrose, Roger, 207-8, 274 

perceptions, as influenced by expectations and interpretations, 31 
perceptrons, 131-35 

Perceptrons (Minsky and Papert), 134-35 ,134 
phenylethylamine, 118 

Philosophical Investigations (Wittgenstein), 221 
phonemes, 61, 135, 137, 146, 152 
photons, 20-21 
physics, 37 

computational capacity and, 281, 316n-19n 

laws of, 37, 267 

standard model of, 2 

see also quantum mechanics 

Pinker, Steven, 76-77, 278 

pituitary gland, 77 

Plato, 212, 221, 231 

pleasure, in old and new brains, 104-8 

Poggio, Tomaso, 85, 159 

posterior ventromedial nucleus (VMpo), 99-100, 99 
prairie vole, 119 

predictable outcomes, determined outcomes vs., 26, 239 

President’s Council of Advisors on Science and Technology, 269 
price/performance, of computation, 4-5, 250-51, 257, 257, 267-68, 301n- 
3 n 

Principia Mathematica (Russell and Whitehead), 181 
probability fields, 218-19, 235-36 
professional knowledge, 39-40 
proteins, reverse-engineering of, 4-5 

qualia, 203-5, 210, 211 
quality of life, perception of, 277-78 
quantum computing, 207-9, 274 
quantum mechanics, 218-19 
observation in, 218-19, 235-36 
randomness vs. determinism in, 236 
Quinlan, Karen Ann, 101 

Ramachandran, Vilayanur Subramanian “Rama,” 230 

random access memory: 

growth in, 259, 260, 301n-3n, 306n-7n 

three-dimensional, 268 

randomness, determinism and, 236 

rationalization, see confabulation 

reality, hierarchical nature of, 4, 56, 90, 94, 172 

recursion, 3, 7-8, 56, 65, 91, 153, 156, 177, 188 

“Red” (Oluseum), 204 

redundancy, 9, 39-40, 64, 184, 185, 197, 224 
in genome, 271, 314n, 315n 
of memories, 59 

of pattern recognition modules, 42, 43, 48, 91 
thinking and, 57 
religious ecstacy, 118 

“Report to the President and Congress, Designing a Digital Future” 
(President’s Council of Advisors on Science and Technology), 269 
retina, 95 

of biological systems, 4-5 

of human brain, see brain, human, computer emulation of; neocortex, 

Rosenblatt, Frank, 131, 133, 134, 135, 191 

Roska, Boton, 94 
Rothblatt, Martine, 278 

routine tasks, as series of hierarchical steps, 32-33 

Rowling, J. K., 117, 121 

Roxy Music, 118 

Russell, Bertrand, 104, 181, 220 

Rutter, Brad, 165 

saccades, 73 
Salk Institute, 89 
same-sex marriage, 278 
Sandberg, Anders, 129-30, 318n 
Schopenhauer, Arthur, 235, 240 

as based on objective measurement, 211 
specialization in, 115 
Science, 82-83 

“scientist’s pessimism,” 272-73 

Searle, John, 170, 201, 205, 206, 222 

“Chinese room” thought experiment of, 170, 274-75 

Seinfeld (TV show), 75 

selective serotonin reuptake inhibitors, 106 

self-organizing systems, 144, 147, 149, 150, 154-55, 162, 168, 171-72, 
175, 197, 270 
sensorimotor area, 77 
sensory cortex, 99 
sensory nerves, 99 
sensory organs, 58 
sensory receptors, 99 

sensory-touch pathway, 58, 60, 94-98, 95, 97-100, 97, 99 

serotonin, 105, 106, 107, 118 

Seung, Sebastian, 10 

Sex and the City (TV show), 117 

sexual reproduction, 118 

simulated, 148 

Shakespeare, William, 39, 114-15, 209 
Shannon, Claude, 183-84, 190 
Shashua, Amnon, 159 
Shaw, J. C., 181 

Short Circuit (film), 210 
Simon, Herbert A., 37-38, 181 
singularity, 194 

Singularity Is Near, The (Kurzweil), 4, 5, 196, 251, 253, 256, 257, 267, 
268-69, 271, 274, 316n-19n 

“Singularity Isn’t Near, The” (Allen and Greaves), 266-72 

Siri, 7, 72, 116, 123, 153, 161-62, 164, 168, 171 

size parameters, 42, 60, 66, 67, 73-74, 91-92 

size variability parameters, 42, 49-50, 67, 73-74, 91-92 

Skinner, B. F., 13 

Smullyan, Raymond, 241 

Society of Mind, The (Minsky), 62, 199 

Sonnet 73 (Shakespeare), 114-15 

SOPA (Stop Online Piracy Act), 279 

sparse coding, 95-96, 135-41 

specialization, increasing, 115 

spectrograms, 135, 136,137 

speech recognition software, 49-50, 51, 53, 61, 72-74, 92, 115-16, 122-23, 
128, 135-46, 145, 198, 273 
GAs in, 149-50, 152 
HHMM in, 149-50, 152-53 
speed of light, 281 

Einstein’s thought experiments on, 18-23 

Sperry, Roger W., 218 

spinal cord, 36, 99 

spindle neurons, 109-11 

split-brain patients, 70, 226-27 

Stanford Encyclopedia of Philosophy, The, 232 

Star Trek: The Next Generation (TV show), 210 

Star Wars films, 210 

stochastic variation, 9 

supercomputer power, growth in, 258, 301n-3n 

as evolutionary goal, 79, 104, 242 
as individual goal, 242 
Sutherland, Stuart, 211 
SyNAPSE chips, 195, 196 
Szent-Gyorgyi, Albert, 93 

taboos, dreams and, 71-72 
Taylor, J. G., 75 

technology, as compensating for human limitation, 3, 27, 276, 279 

Technology Review, 266 

Tegmark, Max, 208 

Terminator films, 210 

testosterone, 118 

thalamus, 36, 77, 95, 97, 97, 98-101 

as gateway to neocortex, 100-101 

thermodynamics, 177 

laws of, 37, 267 

Thiel, Peter, 156 


computing compared with, 26-27 
disorderliness of, 55, 69 
language as translation of, 56, 68 
limitations to, 23-24, 27 
redundancy and, 57 
as statistical analysis, 170 
statistical probability and, 270-71 
thought experiments on, 24, 25-33 
undirected vs. directed, 54-55, 68-69 
see also hierarchical thinking 
thought experiments, 114 
“Chinese room,” 170, 274-75 
on computer consciousness, 202, 210 
of Darwin, 14-16, 23 
of Einstein, 18-23, 114, 117 
on identity, 242-47 
on the mind, 199-247 
on thinking, 24, 25-33 
of Turing, 185-87, 188 
Thrun, Sebastian, 158 

time, Einstein’s thought experiments on, 19-20 
tool making, by humans, 3, 27, 276, 279 
Tractatus Logico-Philosophicus (Wittgenstein), 219-21 
Transcend: Nine Steps to Living Well Forever (Kurzweil and Grossman), 

Transformers films, 210 


per chip, growth in, 258, 301n-3n 

price decrease in, 260, 304n-6n 

three-dimensional, 268 

Turing, Alan, 121, 159-60, 185, 191 

thought experiments of, 185-87, 188 

unsolvable problem theorem of, 187, 207-8 

Turing machine, 185-87, 186, 188, 192, 207-8 

Turing test, 159-60, 169, 170, 178, 191, 213, 214, 233, 276, 298n 

UIMA (Unstructured Information Management Architecture), 167-68 
Ulam, Stan, 194 
Unitarianism, 222 

universality of computation, 26, 181-82, 185, 188, 192, 207 
universe, as capable of encoding information, 2 
University College (London), 118 
unsolvable problems, Turing’s theorem of, 187, 207-8 

vasopressin, 119 

vector quantization, 135, 138-39, 145 

invariance and, 141 

ventral pallidum, 105 

Vicarious Systems, 156 

visual association, 77 

visual cortex, 7, 77, 83, 95, 193 

of congenitally blind people, 87 

digital simulation of, 128 

hierarchical structure of, 85-86 

VI region, 83, 85, 87, 95, 100 

V2 region of, 83, 85, 87, 95 

V5 (MT) region of, 83, 95 

visual information processing, 94-96, 95, 96 

visual pathway, 95 

visual recognition systems, 53 

von Neumann, John, 179, 186-89, 190, 195 

brain/computer comparison of, 191-95 

stored program concept of, 186-87, 188 

von Neumann machine, 187-89, 190, 193 

Voyage of the Beagle (Darwin), 14 

Wall-E (film), 210 

Watson (IBM computer), 6-7, 108, 157-58, 159, 160, 165, 166, 167-68, 
171, 172, 178, 200, 232-33, 239, 247, 265, 270-71, 274 
Watson, James D., 8-9, 16-17 
Watts, Lloyd, 96 

wave function, collapse of, 218-19, 235-36 
Wedeen, Van J., 82-83, 90, 129, 262 
Werblin, Frank S., 94-95 
Whitehead, Alfred North, 181 

Whole Brain Emulation: A Roadmap (Sandberg and Bostrom), 129-30, 

Wiener, Norbert, 115, 143 

Wikipedia, 6, 156, 166, 170, 176, 232, 270, 279 

Wittgenstein, Ludwig, 219-21 

Wolfram, Stephen, 170-71, 177, 236-39 

Wolfram Alpha, 161, 170-72, 177 

Wolfram Research, 170-71 

working memory, 101 

World War I, 278 

World War II, 187, 278 

writing, as backup system, 123-24 

Young, Thomas, 18 

Z-3 computer, 189 
Zuo, Yi, 89 
Zuse, Konrad, 189 


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